Alessandro Saetti 

 
  Address:    Dipartimento di Ingegneria dell'Informazione 
              Facoltà di Ingegneria, Università degli Studi di Brescia 
              Via Branze 38, I-25123 Brescia, Italy.

  Tel.: +39-030-3715521     Tel.: +39-030-380014  E-Mail: saetti(AT)ing.unibs.it 

Alessandro Saetti is associate professor (professore) in Information Processing Systems at the Dept. of Information Engineering of the University of Brescia, Italy. He has been working at the University of Brescia since 2002: from 2002 to 2005 was a Ph.D. student, from 2006 to 2008 a Postdoctoral Researcher, from 2008 to 2016 an assistant professor and since 2016 he has been associate professor.

He was a program committee member or served as a reviewer of many international workshops, conferences and journals. He co-organized the classical track of IPC-5 (The Fifth International Planning Competition), and AI*IA 2010 (The 11th Symposium of the Italian Association for the Artificial Intelligence); he was also co-chair of PlanSig 2010 (The 28th Workshop of the UK Planning and Scheduling Special Interest Group) and 1st AI*IA Doctoral Consortium; currently, he is a PC-member of IJCAI-19 (The 28th International Joint Conference on Artificial Intelligence), ICAPS-19 (The 29th International Conference on Automated Planning and Scheduling), SOCS-19 (The 12th AAAI Symposium on Combinatorial Search), and AAAI-19 (The 33rd AAAI Conference on Artificial Intelligence).

He is author or co-author of more than 70 reviewed papers in various fields of Artificial Intelligence, which have been published in international journals (including "Artificial Intelligence" and "Journal of Artificial Intelligence Research") and conferences (including CP, ECAI, IJCAI, ICAPS, AAAI, SOCS). Most of his scientific work concerns the following aspects: design and analysis of languages for knowledge representation and automated reasoning, computational complexity analysis, development of efficient algorithmic techniques and data structures, implementation of innovative software systems and experimental analysis of the proposed algorithmic techniques. The software systems that he developed, LPGLPG-tdPbP.s and PbP2.q have been awarderd at several editions of the Internation Planning Competition (IPC), which is an important scientific event organized in the context of the International Conference on Planning and Scheduling (ICAPS). 

His research interests focus on temporal reasoning, constraint-based reasoning, anytime planning, machine learning for planning, mixed-initiative planning, plan execution, monitoring and repair, planning with resources and time constraints and HTN-planning.


Main software developed

  • LPG (1999-2004), a fast planner based on local search techniques and planning graphs (with Alfonso Gerevini and Ivan Serina) 
  • Timegraph-III (2003-2005), a C reimplementation of Timegraph-II with some extensions (with Alfonso Gerevini and two undergraduate students)
  • LPG-td (2004-today), an enhanced version of LPG, also working with the language features of PDDL2.2 (with Alfonso Gerevini, Ivan Serina and Paolo Toninelli) 
  • InLPG (2004-today), an interactive environment for plan visualization and generation based on LPG (with Alfonso Gerevini and Maurizio Vitale) Available soon!
  • TGC (2007-today), a fast temporal reasoner for computing the minimal PA-constraints (with Alfonso Gerevini)
  • Duet (2008-09), an automatic planning system combining domain-independent planning and HTN plannning (with Alfonso Gerevini, Ugur Kuter, Dana Nau and Nathaniel Waisbrot)
  • PbP (2008-today), a planning system based on an automatically configurable portfolio of planners (with Alfonso Gerevini and Mauro Vallati)

Scientific awards

Concerning the developed software,
  • LPG has been awarded at IPC-3 (The Third International Planning Competition) as it demonstrated Distinguished Performance of the First Order.
  • LPG-td has been awarded at IPC-4 (The Fourth International Planning Competition) with the 2nd Prize, Suboptimal Metric Temporal Track.
  • PbP.s has received the Best Planning Speed Award and the Best Plan Quality Award at the "learning track" of IPC-6 (The Sixth International Planning Competition)
  • PbP2.q has been awarded at IPC-7 (The Seventh International Planning Competition) as it was the winner of the learning part.

Teaching (in Italian)
Un elenco di proposte di tesi da svolgere con il gruppo di Intelligenza per la Laurea Magistrale in Ingegneria Informatica negli ambiti riguardanti Artificial Intelligence e Machine Learning è disponibile qui

Publications

Journal papers:

  • Andrea Bonisoli, Alfonso Gerevini, Alessandro Saetti, Ivan Serina, A Privacy-Preserving Model for Multi-Agent Propositional Planning, ournal of Experimental & Theoretical Artificial Intelligence, volume 30(4), pp. 481–504, luglio 2018. Taylor & Francis. ISSN 0952-813X

    Abstract: Over the last years, the planning community has formalised several models and approaches to multi-agent (MA) propositional planning. One of the main motivations in MA planning is that some or all agents have private knowledge that cannot be communicated to other agents during the planning process and the plan execution. We argue that the existing models of the multi-agent planning task do not maintain the agents’ privacy when a (strict) subset of the involved agents share confidential knowledge, or when the identity/existence of at least one agent is confidential. In this paper, first we propose a model of the MA-planning tasks that preserves the privacy of the involved agents when this happens. Then we investigate an algorithm based on best first search for our model that uses some new heuristics providing a trade-off between accuracy and agents’ privacy. Finally, an experimental study compares the effectiveness of using the proposed heuristics

  • Francesco Benzi, Alfonso Gerevini, Alessandro Saetti, Ivan Serina, On the use of landmarks in planner LPG, Intelligenza Artificiale, Volume 10(2), pp. 97–111, 2016. IOS-press. ISSN 1724-8035

    Abstract: Domain-Independent planning is known to be a very hard search problem, and in the last three decades many search techniques and heuristics have been developed with the aim of efficiently solving such a task. These techniques and heuristics include the usage of landmarks, which are logical expressions consisting of facts that become true or actions that are executed in any solution plan. We propose the usage of landmarks for speeding up the search of the planner LPG, a system implementing a planning approach based on the use of local search in the space of the action graphs of the planning problem. The results of an experimental evaluation of the proposed techniques show that these techniques can improve the performance of LPG, obtaining a planning system that performs similarly to the state-of-the-art planner LAMA. Moreover, we introduce and experimentally evaluate the concept of “quasi-landmarks”; these are facts that are likely to become true in every solution plan, or facts that must become true in a subset of the solution plans

  • Mauro Vallati, Ivan Serina, Alessandro Saetti, Alfonso Gerevini, Identifying and Exploiting Features for Effective Plan Retrieval in Case-Based Planning, Fundamenta Informaticae, Volume 149, pp. 209–240, settembre 2016. IOS-press. ISSN 0169-2968

    Abstract: Case-based planning can fruitfully exploit knowledge gained by solving a large number of problems, storing the corresponding solutions in a plan library and reusing them for solving similar planning problems in the future. Case-based planning is very effective when similar reuse candidates can be efficiently and effectively chosen. In this paper, we study an innovative technique based on planning problem features for efficiently retrieving solved planning problems (and relative plans) from large plan libraries. A problem feature is a characteristic –usually provided under the form of a number– of the instance that can be automatically derived from the problem specification, domain and search space analyses, or different problem encodings. Given a planning problem to solve, its features are extracted and compared to those of problems stored in the case base, in order to identify most similar problems. Since the use of existing planning features is not always able to effectively distinguish between problems within the same planning domain, we introduce a large number of new features. An experimental analysis in this paper investigates the best set of features to be exploited for retrieving plans in case-based planning, and shows that our feature-based retrieval approach can significantly improve the performance of a state-of-the-art case-based planning system

  • Giuseppe De Giacomo, Alfonso Gerevini, Fabio Patrizi, Alessandro Saetti, Sebastian Sardina, Agent Planning Programs, Artificial Intelligence, Volume 231(1), pp. 64–106, febbraio 2016. Elsevier Publisher. ISSN 0004-3702. (PDF file)

    Abstract: This work proposes a novel high-level paradigm, agent planning programs, for modeling agents behavior, which suitably mixes automated planning with agent-oriented programming. Agent planning programs are finite-state programs, possibly containing loops, whose atomic instructions consist of a guard, a maintenance goal, and an achievement goal, which act as precondition-invariance-postcondition assertions in program specification. Such programs are to be executed in possibly nondeterministic planning domains and their execution requires generating plans that meet the goals specified in the atomic instructions, while respecting the program control flow. In this paper, we define the problem of automatically synthesizing the required plans to execute an agent planning program, propose a solution technique based on model checking of two-player game structures, and use it to characterize the worst-case computational complexity of the problem as EXPTIME-complete. Then, we consider the case of deterministic domains and propose a different technique to solve agent planning programs, which is based on iteratively solving classical planning problems and on exploiting goal preferences and plan adaptation methods. Finally, we study the effectiveness of this approach for deterministic domains through an experimental analysis on well-known planning domains

  • Andrea Bonisoli, Alfonso Gerevini, Alessandro Saetti, Mauro Vallati, Effective Plan Retrieval in Case-based Planning for Metric-Temporal ProblemsJournal of Experimental & Theoretical Artificial Intelligence, Volume 27(5), pp. 603–647, febbraio 2015. Taylor & Francis. (PDF file)

    Abstract: Case-based planning (CBP) is an approach to planning where previous planning experience stored in a case base provides guidance to solving new problems. Such a guidance can be extremely useful when the new problem is very hard to solve, or the stored previous experience is highly valuable (because, e.g. it was provided and/or validated by human experts) and the system should try to reuse it as much as possible. In this work, we address CBP in PDDL domains with real-valued fluents, action durations and timed-initial literals, which are essential to model real-world planning problems involving continuous resources and temporal constraints. We propose some new heuristic techniques for retrieving a plan from a library of existing plans that is promising for solving a new planning problem encountered by the CBP system, i.e. that can be efficiently adapted to solve the new problem. The effectiveness of these techniques, which derive much of their power from the proposed use of the numerical/temporal information in the planning problem specification and in the library plans, is evaluated through an experimental analysis. 

  • Alfonso Gerevini, Alessandro Saetti, Mauro Vallati, Exploiting Macro-actions and Predicting Plan Length in Planning as SatisfiabilityAI Communications, volume 28(2):323-344, 2015. IOS-press. (PDF file) 

    Abstract: The use of automatically learned knowledge for a planning domain can significantly improve the performance of a generic planner when solving a problem in this domain. In this work, we focus on the well-known SAT-based approach to planning and investigate two types of learned knowledge: macro-actions and planning horizon. Macro-actions are sequences of actions that typically occur in the solution plans, while a planning horizon of a problem is the length of a (possibly optimal) plan solving it. We propose a method that uses a machine learning tool for building a predictive model of the optimal planning horizon, and variants of the well-known planner SatPlan and solver MiniSat that can exploit macro actions and learned planning horizons to improve their performance. An experimental analysis illustrates the effectiveness of the proposed techniques demonstrating that significant speedups can be obtained. 

  • Alfonso Gerevini, Alessandro Saetti, Mauro Vallati, Planning through Automatic Portfolio Configuration: The PbP ApproachJournal of Artificial Intelligence Research, volume 50:639-696, 2014. AAAI Press. (PDF file) 

    Abstract: In the field of domain-independent planning, several powerful planners implementing different techniques have been developed. However, no one of these systems outperforms all others in every known benchmark domain. In this work, we propose a multi-planner approach that automatically configures a portfolio of planning techniques for each given domain. The configuration process for a given domain uses a set of training instances to: (i) compute and analyze some alternative sets of macro-actions for each planner in the portfolio identifying a (possibly empty) useful set, (ii) select a cluster of planners, each one with the identified useful set of macro-actions, that is expected to perform best, and (iii) derive some additional information for configuring the execution scheduling of the selected planners at planning time. The resulting planning system, called PbP (Portfolio-based Planner), has two variants focusing on speed and plan quality. Different versions of PbP entered and won the learning track of the sixth and seventh International Planning Competitions. In this paper, we experimentally analyze PbP considering planning speed and plan quality in depth. We provide a collection of results that help to understand PbP.s behavior, and demonstrate the effectiveness of our approach to configuring a portfolio of planners with macro-actions. 

  • Alfonso Gerevini, Alessandro Saetti, Ivan Serina, Planning in Domains with Derived Predicates through Rule-Action Graphs and Local SearchAnnals of Mathematics and Artificial Intelligence, volume 62(3):259-298, 2011. Springer. (PDF file) 

    Abstract: The ability to express derived predicates in the formalization of a planning domain is both practically and theoretically important. In this paper, we propose an approach to planning with derived predicates where the search space consists of "Rule-Action Graphs", particular graphs of actions and rules representing derived predicates. We propose some techniques for representing such rules and reasoning with them, which are integrated into a framework for planning through local search and rule-action graphs. We also present some heuristics for guiding the search of a rule-action graph representing a valid plan. Finally, we analyze our approach through an extensive experimental study aimed at evaluating the importance of some specific techniques for the performance of the approach. The results of our experiments also show that our planner performs quite well compared to other state-of-the-art planners handling derived predicates. 

  • Alfonso Gerevini, Alessandro Saetti, Computing the Minimal Relations in Point-based Qualitative Temporal Reasoning through Metagraph ClosureArtificial Intelligence, volume 175(2):556-585, 2011. Elsevier Publisher. (PDF file) 

    Abstract: Computing the minimal representation of a given set of constraints (a CSP) over the Point Algebra (PA) is a fundamental temporal reasoning problem. The main property of a minimal CSP over PA is that the strongest entailed relation between any pair of variables in the CSP can be derived in constant time. We study some new methods for solving this problem which exploit and extend two prominent graph-based representations of a CSP over PA: the timegraph and the series-parallel (SP) metagraph. Essentially, these are graphs partitioned into sets of chains and series-parallel subgraphs, respectively, on which the search is supported by a metagraph data structure. The proposed approach is based on computing the metagraph closure for these representations, which can be accomplished by some methods studied in the paper. In comparison with the known techniques based on enforcing path consistency, under certain conditions about the structure of the input CSP and the size of the generated metagraph, the proposed metagraph closure approach has better worst-case time and space complexity. Moreover, for every sparse CSP over the convex PA, the time complexity is reduced to O(n2) from O(n3), where n is the number of variables involved in the CSP. An extensive experimental analysis presented in the paper compares the proposed techniques and other known algorithms. These experimental results identify the best performing methods and show that, in practice, for CSPs exhibiting chain or SP-graph structure and randomly generated (both sparse and dense) CSPs, the metagraph closure approach is significantly faster than the approach based on enforcing path consistency.

  • Alfonso Gerevini, Alessandro Saetti, Ivan Serina, An Empirical Analysis of Some Heuristic Features for Planning through Local Search and Action GraphsFundamenta Informaticae, volume 107, pp. 167-197, 2011. IOS Press. (PDF file)

    Abstract: Planning through local search and action graphs is a powerful approach to fully-automated planning which is implemented in the well-known LPG planner. The approach is based on a stochastic local search procedure exploring a space of partial plans and several heuristic features with different possible options. In this paper, we experimentally analyze the most important of them, with the goal of understanding and evaluating their impact on the performance of LPG, and of identifying default settings that work well on a large class of problems. In particular, we analyze several heuristic techniques for (a) evaluating the search neighborhood, (b) defining/restricting the search neighborhood, (c) selecting the next plan flaw to handle, (d) setting the "noise" parameter randomizing the search, and (e) computing reachability information that can be exploited by the heuristic functions used to evaluate the neighborhood elements. Some of these techniques were introduced in previous work on LPG, while others are new. Additional experimental results indicate that the current version of LPG using the identified best heuristic techniques as the default settings is competitive with the winner of the last (2008) International Planning Competition.

  • Alfonso Gerevini, Patrik Haslum, Derek Long, Alessandro Saetti, Yannis Dimopoulos, Deterministic Planning in the Fifth International Planning Competition: PDDL3 and Experimental Evaluation of the PlannersArtificial Intelligence, volume 173(5-6), pp. 619--668, 2009. Elsevier Publisher. (PDF file)

    Abstract: The international planning competition (IPC) is an important driver for planning research. The general goals of the IPC include pushing the state of the art in planning technology by posing new scientific challenges, encouraging direct comparison of planning systems and techniques, developing and improving a common planning domain definition language, and designing new planning domains and problems for the research community. This paper focuses on the deterministic part of the fifth international planning competition (IPC5), presenting the language and benchmark domains that we developed for the competition, as well as a detailed experimental evaluation of the deterministic planners that entered IPC5, which helps to understand the state of the art in the field. We introduce an extension of PDDL, called PDDL3, allowing the user to express strong and soft constraints about the structure of the desired plans, as well as strong and soft problem goals. We discuss the expressive power of the new language focusing on the restricted version that was used in IPC5, for which we give some basic results about its compilability into PDDL2. Moreover, we study the relative performance of the IPC5 planners in terms of solved problems, CPU time, and plan quality; we analyse their behaviour with respect to the winners of the previous competition; and we evaluate them in terms of their capability of dealing with soft goals and constraints, and of finding good quality plans in general. Overall, the results indicate significant progress in the field, but they also reveal that some important issues remain open and require further research, such as dealing with strong constraints and computing high quality plans in metric-time domains and domains involving soft goals or constraints.

  • Alfonso Gerevini, Alessandro Saetti, Ivan Serina, An Approach to Efficient Planning with Numerical Fluents and Multi-Criteria Plan QualityArtificial Intelligence, volume 172(8-9), pp. 899-944, 2008. Elsevier Publisher. (PDF file)

    Abstract: Dealing with numerical information is practically important in many real-world planning domains where the executability of an action can depend on certain numerical conditions, and the action effects can consume or renew some critical continuous resources, which in PDDL can be represented by numerical fluents. When a planning problem involves numerical fluents, the quality of the solutions can be expressed by an objective function that can take different plan quality criteria into account. We propose an incremental approach to automated planning with numerical fluents and multi-criteria objective functions for PDDL numerical planning problems. The techniques in this paper significantly extend the framework of planning with action graphs and local search implemented in the LPG planner. We define the numerical action graph (NA-graph) representation for numerical plans and we propose some new local search techniques using this representation, including a heuristic search neighborhood for NA-graphs, a heuristic evaluation function based on relaxed numerical plans, and an incremental method for plan quality optimization based on particular search restarts. Moreover, we analyze our approach through an extensive experimental study aimed at evaluating the importance of some specific techniques for the performance of the approach, and at analyzing its effectiveness in terms of fast computation of a valid plan and quality of the best plan that can be generated within a given CPU-time limit. Overall, the results show that our planner performs quite well compared to other state-of-the-art planners handling numerical fluents.

  • Alfonso Gerevini, Alessandro Saetti, Ivan Serina, An Approach to Temporal Planning and Scheduling in Domains with Predictable Exogenous EventsJournal of Artificial Intelligence Research (JAIR), volume 25, pp. 187-231, 2006. AAAI Press. (PDF file)

    Abstract: The treatment of exogenous events in planning is practically important in many real-world domains where the preconditions of certain plan actions are affected by such events. In this paper we focus on planning in temporal domains with exogenous events that happen at known times, imposing the constraint that certain actions in the plan must be executed during some predefined time windows. When actions have durations, handling such temporal constraints adds an extra difficulty to planning. We propose an approach to planning in these domains which integrates constraint-based temporal reasoning into a graph-based planning framework using local search. Our techniques are implemented in a planner that took part in the 4th International Planning Competition (IPC-4). A statistical analysis of the results of IPC-4 demonstrates the effectiveness of our approach in terms of both CPU-time and plan quality. Additional experiments show the good performance of the temporal reasoning techniques integrated into our planner.

  • Alfonso Gerevini, Alessandro Saetti, Ivan Serina.  Planning through Stochastic Local Search and Temporal Action Graphs in LPGJournal of Artificial Intelligence Research (JAIR), volume 20, pp. 239-290, 2003. Morgan Kaufmann Publishers. (PDF file)

    Abstract: We present some techniques for planning in domains specified with the recent standard language PDDL2.1, supporting durative actions and numerical quantities. These techniques are implemented in LPG, a domain-independent planner that took part in the 3rd International Planning Competition (IPC). LPG is an incremental, any time system producing multi-criteria quality plans. The core of the system is based on a stochastic local search method and on a graph-based representation called "Temporal Action Graphs" (TA-graphs). This paper focuses on temporal planning, introducing TA-graphs and proposing some techniques to guide the search in LPG using this representation. The experimental results of the 3rd IPC, as well as further results presented in this paper, show that our techniques can be very effective. Often LPG outperforms all other fully-automated planners of the 3rd IPC in terms of speed to derive a solution, or quality of the solutions that can be produced. 

Conference papers:

  • Alfonso Gerevini, Nir Lipovetzky, Nico Peli, Francesco Percassi, Alessandro Saetti, Ivan Serina, Novelty Messages Filtering for Multi Agent Privacy-preserving Planning, Proceedings of the Twelfth Annual Symposium on Combinatorial Search (SOCS-19), Napa, California (USA), 16–17 luglio, 2019. AAAI Press.
  • Abstract: In multi-agent planning, agents jointly compute a plan that achieves mutual goals, keeping certain information private to the individual agents. Agents’ coordination is achieved through the transmission of messages. These messages can be a source of privacy leakage as they can permit a malicious agent to collect information about other agents’ actions and search states. In this paper, we investigate the usage of novelty techniques in the context of (decentralised) multi-agent privacy-preserving planning, addressing the challenges related to the agents’ privacy and performance. In particular, we show that the use of novelty based techniques can significantly reduce the number of messages transmitted among agents, better preserving their privacy and improving their performance. An experimental study analyses the effectiveness of our techniques and compares them with the state-of-the-art. Finally, we evaluate the robustness of our approach, considering different delays in the transmission of messages as they would occur in overloaded networks, due for example to massive attacks or critical situations.

  • Alfonso Gerevini, Nir Lipovetzky, Francesco Percassi, Alessandro Saetti, Ivan Serina, Best-First Width Search for Multi Agent Privacy-preserving Planning, Proceedings of the Twenty-Ninth International Conference on Automated Planning and Scheduling (ICAPS-19), Berkeley, California (USA), 11–15 luglio, 2019. AAAI Press.
  • Abstract: In multi-agent planning, preserving the agents’ privacy has become an increasingly popular research topic. For preserving the agents’ privacy, agents jointly compute a plan that achieves mutual goals by keeping certain information private to the individual agents. Unfortunately, this can severely restrict the accuracy of the heuristic functions used while searching for solutions. It has been recently shown that, for centralized planning, the performance of goal oriented search can be improved by combining goal oriented search and width-based search. The combination of these techniques has been called best-first width search. In this paper, we investigate the usage of best-first width search in the context of (decentralised) multi-agent privacy-preserving planning, addressing the challenges related to the agents’ privacy and performance. In particular, we show that best-first width search is a very effective approach over several benchmark domains, even when the search is driven by heuristics that roughly estimate the distance from goal states, computed without using the private information of other agents. An experimental study analyses the effectiveness of our techniques and compares them with the state-of-the-art.

  • Mattia Chiari, Shizhe Zhao, Adi Botea, Alfonso Gerevini, Daniel Harabor, Alessandro Saetti, Matteo Salvetti, Peter J. Stuckey, Cutting the Size of Compressed Path Databases With Wildcards and Redundant Symbols, Proceedings of the Twenty-Ninth International Conference on Automated Planning and Scheduling (ICAPS-19), Berkeley, California (USA), 11–15 luglio, 2019. AAAI Press.
  • Abstract: Path planning on gridmaps is a common problem in AI and a popular topic in application areas such as computer games. Compressed Path Databases (CPDs) represent a state-of-the-art approach to the problem, in terms of the speed of computing full optimal paths and also individual optimal moves. Despite significant improvements in recent years, the memory required to store a CPD can still be a bottleneck for large game maps. In this work we present a new compression approach that can reduce the size of CPDs. Our approach uses an extended notion of wildcards and a novel concept called a redundant symbol. We implement our ideas on top of a state-of-the-art CPD system and, in a range of experiments, we demonstrate a substantial reduction in the size of CPDs.

  • Gabriele Bazzotti, Alfonso Gerevini, Nir Lipovetzky, Francesco Percassi, Alessandro Saetti, Ivan Serina, Iterative Width Search for Multi Agent Privacy-Preserving Planning, Proceedings of the Seventeenth International Conference of the Italian Association for Artificial Intelligence (AI*IA-18), Trento, Italy, 20–23 novembre, 2018. Springer.
  • Abstract: In multi-agent planning, preserving the agents’ privacy has become an increasingly popular research topic. In multi-agent privacy-preserving planning, agents jointly compute a plan that achieves mutual goals by keeping certain information private to the individual agents. Unfortunately, preserving the privacy of such information can severely restrict the accuracy of the heuristic functions used while searching for solutions. Recently, it has been shown that centralized planning based on Width-based search is a very effective approach over several benchmark domains, even when the search is driven by uninformed heuristics. In this paper, we investigate the usage of Width-based search in the context of (decentralised) multi-agent privacy-preserving planning, addressing the challenges related to the agents’ privacy and performance. An experimental study analyses the effectiveness of our techniques and compares them with the state-of-the-art.

  • Matteo Salvetti, Adi Botea, Alfonso Gerevini, Daniel Harabor, Alessandro Saetti, Two-Oracle Optimal Path Planning on Grid Maps, Proceedings of the Twenty-Eigth International Conference on Automated Planning and Scheduling (ICAPS-18), Delft (Olanda), 24–29 giugno 2018. AAAI Press.
  • Abstract: Path planning on grid maps has progressed significantly in recent years, partly due to the Grid-based Path Planning Competition GPPC. In this work we present an optimal approach which combines features from two modern path planning systems, SRC and JPS+, both of which were among the strongest entrants at the 2014 edition of the competition. Given a current state s and a target state t, SRC is used as an oracle to provide an optimal move from s towards t. Once a direction is available we invoke a second JPS-based oracle to tell us for how many steps that move can be repeated, with no need to query SRC between the steps. Experiments on a range of grid maps demonstrate a strong improvement from our combined approach. Against SRC, which remains an optimal solver with state-of-the-art speed, the performance improvement of our new system ranges from comparable to more than one order of magnitude faster.

  • Matteo Salvetti, Adi Botea, Alessandro Saetti, Alfonso Gerevini, Compressed Path Databases with Ordered Wildcard Substitutions, Proceedings of the Twenty-Seventh International Conference on Automated Planning and Scheduling (ICAPS-17), Pittsburgh (USA), 18–23 giugno 2017. AAAI Press.
  • Abstract: Compressed path databases (CPDs) are a state-of-the-art approach to path planning, a core AI problem. In the Grid-based Path Planning Competition, the CPD-based SRC path planning system was the fastest competitor with respect to both computing full optimal paths and computing the first moves of an optimal path. However, on large maps, CPDs can require a significant amount of memory, which can be a serious practical bottleneck. We present an approach that significantly reduces the size of a CPD. Our approach replaces part of the data encoded in a CPD with wildcards (“don’t care” symbols), maintaining the ability to compute optimal paths for all pairs of nodes of an undirected graph. We show that using wildcards in a way that maximizes the memory savings is NP-hard. We consider heuristics that achieve a good performance in practice. We implement our ideas on top of SRC and provide a detailed empirical analysis. Average memory savings can reach a factor of 2. Our first-k-moves lag (i.e., the time before knowing the first k optimal forward moves) increases, but it can be kept within competitive values. The speed of computing full optimal paths improves slightly.

  • Federico Falcone, Alfonso Gerevini, Alessandro Saetti, On Realizing Planning Programs in Domains with Dead-end States, Proceedings of the Tenth Annual Symposium on Combinatorial Search (SOCS-17), Pittsburgh (USA), 16–17 giugno 2017. AAAI Press.
  • Abstract: Agent planning programs are finite-state programs, possibly containing loops, whose atomic instructions consist of a guard, a maintenance goal, and an achievement goal, which act as precondition-invariance-postcondition assertions in program specification. The execution of such programs requires generating plans that meet the goals specified in the atomic instructions, while respecting the program control flow. Recently, De Giacomo et al. (2016) presented a technique, based on iteratively solving classical planning problems with action costs, for realizing planning programs in deterministic domains. Such a technique works generally well for domains with no or very few dead-end states. In this paper, we propose an enhancement of this technique to handle deterministic domains that have potentially many dead-end states, and we study the effectiveness of our technique through an experimental analysis.

  • Mauro Vallati, Ivan Serina, Alessandro Saetti, Alfonso Gerevini, Identifying and Exploiting Features for Effective Plan Retrieval in Case-Based PlanningProceedings of the Twenty-Fifth International Conference on Automated Planning and Scheduling (ICAPS-15), Jerusalem (Israel), 2015. (PDF file)

    Abstract: Case-Based planning can fruitfully exploit knowledge gained by solving a large number of problems, storing the corresponding solutions in a plan library and reusing them for solving similar planning problems in the future. Case-based planning is extremely effective when similar reuse candidates can be efficiently chosen. In this paper, we study an innovative technique based on planning problem features - real number summarising potentially important property of the instance - for efficiently retrieving solved planning problems (and relative plans) from large plan libraries. Since existing planning features are not always able to effectively distinguish between problems within the same planning domain, we introduce a new class of features. Our experimental analysis shows that the proposed features-based retrieval approach can significantly improve the performance of a state-of-the-art case-based planning system.

  • Andrea Bonisoli, Alfonso Gerevini, Alessandro Saetti, Ivan Serina, A Privacy-preserving Model for the Multi-agent Propositional Planning ProblemProceedings of the Twenty-First European Conference on Artificial Intelligence (ECAI-14), Prague (Czech Republic), 2014. (PDF file)

    Abstract: Over the last years, the planning community has formalized several models and approaches to multi-agent (MA) planning. One of the main motivations in MA planning is that some or all agents have private knowledge that cannot be communicated to other agents during the planning process and the plan execution. In this paper, we propose a model that preserves the privacy of the involved agents, and discuss how the MA-A* search algorithm can be adapted to implement our model.

  • Alfonso Gerevini, Anna Roubickova, Alessandro Saetti, Ivan Serina, Offline and Online Plan Library Maintenance in Case-based PlanningProceedings of the Thirteenth Conference of the Italian Association for Artificial Intelligence (AIIA-13), Turin (Italy), 2013. (PDF file)

    Abstract: One of the ways to address the high computational complexity of planning is reusing previously found solutions whenever they are applicable to the new problem with limited adaptation. To do so, a reuse planning system needs to store found solutions in a library of plans, also called a case base. The quality of such a library critically influences the performance of the planner, and therefore it needs to be carefully designed and created. For this reason, it may be also important to update the library during the lifetime of the system, as the type of problems being addressed may evolve or differ from the ones the case base was originally designed for. In our ongoing research, we address the problem of maintaining the library of plans in a recent case-based planner called OAKplan. After having developed offline techniques to reduce an oversized library, we introduce here a complementary online approach that attempts to limit the growth of the library, and we consider the combination of offline and online techniques to ensure the best performance of the case-based planner. The different investigated approaches and techniques are then experimentally evaluated and compared.

  • Mauro Vallati, Chris Fawcett, Alfonso Gerevini, Holger H. Hoos, Alessandro Saetti, Automatic Generation of Efficient Domain-Optimized Planners from Generic Parametrized PlannersProceedings of the Sixth Annual Symposium on Combinatorial Search (SOCS-13), Leavenworth (WA), 2013. (PDF file)

    Abstract: When designing state-of-the-art, domain-independent planning systems, many decisions have to be made with respect to the domain analysis or compilation performed during preprocessing, the heuristic functions used during search, and other features of the search algorithm. These design decisions can have a large impact on the performance of the resulting planner. By providing many alternatives for these choices and exposing them as parameters, planning systems can in principle be configured to work well on different domains. However, planners are typically used in default configurations that have been chosen because of their good average performance over a set of benchmark domains, with limited experimentation over the potentially huge range of possible configurations. In this work, we propose a general framework for automatically configuring a parameterized planner, and show that substantial performance gains can be achieved. We apply the framework to the well-known LPG planner, which in the context of this work was expanded to 62 parameters and over 6.5x1017 possible configurations. By using this highly parameterized planning system in combination with the state-of-the-art automatic algorithm configuration procedure ParamILS, excellent performance on a broad range of well-known benchmark domains was achieved, as also witnessed by the results of the learning track of the 7th International Planning Competition.

  • Alfonso Gerevini, Anna Roubickova, Alessandro Saetti, Ivan Serina, On the Plan-library Maintenance Problem in a Case-based PlannerProceedings of the 21st International Conference on Case-Based Reasoning (ICCBR-13), Saratoga Springs (NY), 2013. (PDF file)

    Abstract: Case-based planning is an approach to planning where previous planning experience stored in a case base provides guidance to solving new problems. Such a guidance can be extremely useful when the new problem is very hard to solve, or the stored previous experience is highly valuable (because, e.g., it was provided and/or validated by human experts) and the system should try to reuse it as much as possible. However, as known in general case-based reasoning, the case base needs to be maintained at a manageable size, in order to avoid that the computational cost of querying it excessively grows, making the entire approach ineffective. We formally define the problem of case base maintenance for planning, discuss which criteria should drive a successful policy to maintain the case base, introduce some policies optimizing different criteria, and experimentally analyze their behavior by evaluating their effectiveness and performance.

  • Alfonso Gerevini, Alessandro Saetti, Ivan Serina, Case-based Planning for Problems with Real-valued Fluents: Kernel Functions for Effective Plan RetrievalProceedings of the 20th European Conference on Artificial Intelligence (ECAI-12), Montpellier (France), 2012. (PDF file)

    Abstract: Case-based planning (CBP) re-uses existing plans as a starting point to solve new planning problems. In this work, we address CBP for planning in PDDL domains with real-valued fluents, that are essential to model real-world problems involving continuous resources. Specifically, we propose some new heuristic techniques for retrieving a plan from a library of existing plans that is promising for a new given planning problem, i.e., that can be efficiently adapted to solve the new problem. The effectiveness of these techniques, which derive much of their power from the use of the numerical information in the planning problem specification and in the library plans, is then evaluated by an experimental analysis.

  • Alfonso Gerevini, Alessandro Saetti, Fabio Patrizi, An Effective Approach to Realizing Planning ProgramsProceedings of the Twenty-First International Conference on Automated Planning and Scheduling (ICAPS-11), Freiburg (Germany), 2011. (PDF file)

    Abstract: Planning programs are loose, high-level, declarative representations of the behavior of agents acting in a domain and following a path of goals to achieve. Such programs are specified through transition systems that can include cycles and decisions to make at certain points. We investigate a new effective approach for solving the problem of realizing a planning program, i.e., informally, for finding and combining a collection of plans that guarantee the planning program executability. We focus on deterministic domains and propose a general algorithm that solves the problem exploiting a planning technique handling goal constraints and preferences. A preliminary experimental analysis indicates that our approach dramatically outperforms the existing method based on formal verification and synthesis techniques.

  • Alfonso Gerevini, Alessandro Saetti, Ivan Serina, Temporal Planning with Problems Requiring Concurrency through Action Graphs and Local SearchProceedings of the Twentieth International Conference on Automated Planning and Scheduling (ICAPS-10), Toronto (Canada), 2010. (PDF file)

    Abstract: We present an extension of the planning framework based on action graphs and local search to deal with PDDL2.1 temporal problems requiring concurrency, while previously the approach could only solve problems admitting a sequential solution. The paper introduces a revised plan representation supporting concurrency and some new search techniques using it, which are implemented in a new version of the LPG planner. An experimental analysis indicates that the proposed approach is suitable to temporal planning with requiring concurrency and is competitive with state-of-the-art planners.

  • Alfonso Gerevini, Alessandro Saetti, Mauro Vallati, An Automatically Configurable Portfolio-based Planner with Macro-actions: PbPProceedings of the Nineteenth International Conference on Automated Planning and Scheduling (ICAPS-09), Thessaloniki (Greece), 2009. (PDF file)

    Abstract: While several powerful domain-independent planners have recently been developed, no one of these clearly outperforms all the others in every known benchmark domain. We present PbP, a multi-planner which automatically configures a portfolio of planners by (i) computing some sets of macro-actions for every planner in the portfolio, (ii) selecting a promising combination of planners in the portfolio and relative useful macro-actions, and (iii) defining some running time slots for their round-robin scheduling during planning. The configuration relies on some knowledge about the performance of the planners in the portfolio and relative macro-actions which is automatically generated from a training problem set. PbP entered the learning track of IPC-2008 and was the overall winner of this competition track. An experimental study confirms the effectiveness of PbP, and shows that the learned configuration knowledge is useful for PbP.

  • Alfonso Gerevini, Ugur Kuter, Dana S. Nau, Alessandro Saetti, Nathaniel Waisbrot, Combining Domain-Independent Planning and HTN Planning: The Duet PlannerProceedings of the 18th European Conference on Artificial Intelligence (ECAI-08), Patras (Greece), 2008. (PDF file)

    Abstract: Despite the recent advances in planning for classical domains, the question of how to use domain knowledge in planning is yet to be completely and clearly answered. Some of the existing planners use domain-independent search heuristics, and some others depend on intensively-engineered domain-specific knowledge to guide the planning process. In this paper, we describe an approach to combine ideas from both of the above schools of thought. We present DUET, our planning system that incorporates the ability of using hierarchical domain knowledge in the form of Hierarchical Task Networks (HTNs) as in SHOP2 and using domain-independent local search techniques as in LPG. In our experiments, DUET was able to solve much larger problems than LPG could solve, with only minimal domain knowledge encoded in HTNs (much less domain knowledge than SHOP2 needed to solve those problems by itself).

  • Alfonso Gerevini, Alessandro Saetti Efficient Computation of Minimal Point Algebra Constraints by Metagraph ClosureProceedings of the 13th International Conference on Principles and Practice of Constraint Programming (CP-07), pp. 301-316, Providence (USA), 2007. (PDF file)

    Abstract: Computing the minimal network (or minimal CSP) representation of a given set of constraints over the Point Algebra (PA) is a fundamental reasoning problem. In this paper we propose a new approach to solving this task which exploits the timegraph representation of a CSP over PA. A timegraph is a graph partitioned into a set of chains on which the search is supported by a metagraph data structure. We introduce a new algorithm that, by making a particular closure of the metagraph, extends the timegraph with information that supports the derivation of the strongest implied constraint between any pair of point variables in constant time. The extended timegraph can be used as a representation of the minimal CSP. We also compare our method and known techniques for computing minimal CSPs over PA. For CSPs that are sparse or exhibit chain structure, our approach has a better worst-case time complexity. Moreover, an experimental analysis indicates that the performance improvements of our approach are practically very significant. This is the case especially for CSPs with a chain structure, but also for randomly generated (both sparse and dense) CSPs.

  • Alfonso Gerevini, Alessandro Saetti, Ivan Serina, Paolo Toninelli, Fast Planning in Domains with Derived Predicates: An Approach Based on Rule-Action Graphs and Local SearchProceedings of the 20th National Conference on Artificial Intelligence (AAAI-05), pp. 1157-1162, Pittsburgh (USA), 2005. (PDF file)

    Abstract: The ability to express "derived predicates" in the formalization of a planning domain is both practically and theoretically important. We propose an approach to planning with derived predicates where the search space consists of particular graphs of actions and rules, called rule-action graphs, representing partial plans. We present some techniques for managing domain rules in the context of a local search process for rule-action graphs, and new heuristics for guiding the search. The proposed approach and techniques are implemented in a new version of the LPG planner, which took part in the fourth International Planning Competition showing good performance in many benchmark problems.

  • Alfonso Gerevini, Alessandro Saetti, Ivan Serina, Integrating Planning and Temporal Reasoning for Domains with Durations and Time WindowsProceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI-05), pp. 1226-1235, Edinburgh (UK), 2005. (PDF file)

    Abstract: The treatment of exogenous events in planning is practically important in many domains. In this paper we focus on planning with exogenous events that happen at known times, and affect the plan actions by imposing that the execution of certain plan actions must be during some time windows. When actions have durations, handling such constraints add an extra difficulty to planning, which can be obtained by integrating temporal reasoning into planning. We propose a new approach to planning in domains with durations and time windows, which combines graph-based planning and disjunctive constraint-based temporal reasoning. Our techniques are implemented in a planner that took part in the 4th International Planning Competition showing very good performance in many benchmark problems. 

  • Alfonso Gerevini, Alessandro Saetti, Ivan Serina, Planning with Numerical Expressions in LPG Proceedings of the 16th European Conference on Artificial Intelligence (ECAI-04), pp. 667-671, Valencia (Spain), 2004. (PDF file)

    Abstract: We present some techniques for handling planning problems with numerical expressions that can be specified using the standard planning language PDDL. These techniques are implemented in LPG, a fully-automated planner based on local search that was awarded at the third international planning competition (2002). First, we present a plan representation for handling numerical expressions called Numerical Action Graph (NA-graph). Then, we propose some extensions of LPG to guide a search process where the search states are NA-graphs. Finally, we give some experimental results showing that our techniques are very effective in terms of CPU-time or plan quality, and that they significantly improve the previous version of the planner.

  • Alfonso Gerevini, Alessandro Saetti, Ivan Serina, An Empirical Analysis of Some Heuristic Features for Local Search in LPG Proceedings of the 14th International Conference on Automated Planning and Scheduling (ICAPS-04), pp. 171-180, Vancouver (Canada), 2004. (PDF file)

    Abstract: LPG is a planner that performed very well in the last International planning competition (2002). The system is based on a stochastic local search procedure, and it incorporates several heuristic features. In this paper we experimentally analyze the most important of them with the goal of understanding and evaluating their impact on the performance of the planner. In particular, we examine three heuristic functions for evaluating the search neighborhood and some settings of the "noise" parameter, that randomizes the next search step for escaping from local minima. Moreover, we present and analyze additional heuristic techniques for restricting the search neighborhood and for selecting the next inconsistency to handle. The experimental results show that the use of such techniques significantly improves the performance of the planner.

  • Alfonso Gerevini, Ivan Serina, Alessandro Saetti, Sergio Spinoni, Local Search for Temporal Planning in LPGProceedings of the 13th International Conference on Automated Planning & Scheduling (ICAPS-03), pp. 62-71, Trento (Italy), 2003. (PDF file)

    Abstract: We present some techniques for planning in temporal domains specified with the recent standard language PDDL2.1. These techniques are implemented in LPG, a fully-automated system that took part in the third International Planning Competition (Toulouse, 2002) showing excellent performance. The planner is based on a stochastic local search method and on a graph-based representation called "Temporal Action Graphs" (TA-graphs). In this paper we present some new heuristics to guide the search in LPG using this representation. An experimental analysis of the performance of LPG on a large set of test problems used in the competition shows that our techniques can be very effective, and that often our planner outperforms all other fully-automated temporal planners that took part in the contest.

Workshop (or satellite event) papers:

  • Matteo Salvetti, Adi Botea, Alessandro Saetti, Alfonso Gerevini, Compressed Path Databases with Ordered Wildcard Substitutions, Proceedings of the Twenty-fourth RCRA International Workshop on Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion (RCRA), Bari, 14 novembre 2017.
  • Federico Falcone, Alfonso Gerevini, Alessandro Saetti, On Realizing Planning Programs in Domains with Dead-end States, Proceedings of the ICAPS Workshop on Generalized Planning, Pittsburgh, 19 giugno 2017.
  • Francesco Benzi, Alfonso Gerevini, Alessandro Saetti, Ivan Serina, On the Use of Landmarks in LPG, Proceedings of the 6th AI*IA Workshop on Planning and Scheduling, Ferrara (Italia), 22 settembre 2015.
  • Andrea Bonisoli, Alfonso Gerevini, Alessandro Saetti, Ivan Serina, A Privacy-preserving Model for the Multi-agent Propositional Planning ProblemWorking notes of the Second Workshop on Distributed and Multi-Agent Planning (DMAP-14), Portsmouth (New Hampshire, USA), 2014.
  • Alfonso Gerevini, Anna Roubickova, Alessandro Saetti, Ivan Serina, Plan-library Maintenance Policies for Case-based PlanningWorking notes of Twenty-third International Conference on Automated Planning & Scheduling (ICAPS-13) - Workshop on Knowledge Engineering for Planning and Scheduling (KEPS), Rome (Italy), 2013.
  • Mauro Vallati, Chris Fawcett, Alfonso Gerevini, Holger Hoos, Alessandro Saetti, Generating Fast Domain-Specific Planners by Automatically Configuring a Generic Parameterised PlannerWorking notes of Twenty-First International Conference on Automated Planning & Scheduling (ICAPS-11) - Workshop on Planning and Learning, Friburgo (Germany), 2011.
  • Alfonso Gerevini, Alessandro Saetti, An Interactive Tool for Plan Visualization, Inspection and GenerationWorking notes of Twenty-First International Conference on Automated Planning & Scheduling (ICAPS-11) - Workshop on Knowledge Engineering for Planning and Scheduling, Friburgo (Germany), 2011.
  • Alfonso Gerevini, Alessandro Saetti, Mauro Vallati, PbP2: Automatic Configuration of a Portfolio-based Multi-PlannerWorking notes of Twenty-First International Conference on Automated Planning & Scheduling (ICAPS-11) - Seventh International Planning Competition, Freiburg (Germany), 2011.
  • Mauro Vallati, Chris Fawcett, Alfonso Gerevini, Holger Hoos, Alessandro Saetti, ParLPG: Generating Domain-Specific Planners through Automatic Parameter Configuration in LPGWorking notes of Twenty-First International Conference on Automated Planning & Scheduling (ICAPS-11) - Seventh International Planning Competition, Freiburg (Germany), 2011.
  • Alfonso Gerevini, Alessandro Saetti, Mauro Vallati, Optimal SAT-based Planning with Macro-actions and Learned HorizonsProceedings of the 4th Italian Workshop on Planning & Scheduling jointly with 28th Workshop of the UK Planning & Scheduling Special Interest Group (IT/UK PlanSIG-10), Brescia (Italy), 2010.
  • Alfonso Gerevini, Alessandro Saetti, Mauro Vallati, Learning and Exploiting Configuration Knowledge for a Portfolio-based plannerWorking notes of Nineteenth International Conference on Automated Planning & Scheduling (ICAPS-09) - Workshop on Planning and Learning, Thessaloniki (Grecia), 2009.
  • Beniamino Galvani, Alfonso Gerevini, Alessandro Saetti, Mauro Vallati, A Planner Based on an Automatically Configurable Portfolio of Domain-independent Planners with Macro-actions: PbPWorking notes of Eighteenth International Conference on Automated Planning & Scheduling (ICAPS-08) - 6th International Planning Competition, Sydney (Australia), 2008.
  • Alfonso Gerevini, Alessandro Saetti. An Interactive Environment for Plan Visualization and Generation: InLPGWorking notes of Eighteenth International Conference on Automated Planning & Scheduling (ICAPS-08) - System Demonstration, Sydney (Australia), 2008.
  • Alfonso Gerevini, Ugur Kuter, Dana S. Nau, Alessandro Saetti, Nathaniel Waisbrot. Combining Domain-Independent Planning and HTN Planning: The Duet Planner.Working notes of Eighteenth International Conference on Automated Planning & Scheduling (ICAPS-08) - Workshop on Knowledge Engineering for Planning and Scheduling (KEPS), Sydney (Australia), 2008.
  • Yannis Dimopoulos, Alfonso Gerevini, Patrik Haslum, Alessandro Saetti, The Benchmark Domains of the Deterministic Part of IPC-5Working notes of the 16th International Conference on Automated Planning & Scheduling (ICAPS-06) - 5th International Planning Competition, Windermere (UK), 2006. 
  • Alfonso Gerevini, Alessandro Saetti, Ivan Serina, Paolo Toninelli, Planning with Derived Predicates through Rule-Action Graphs and Local Search TechniquesAI*IA 2005: Advances in Artificial Intelligence, LNAI 3673, Milan (Italy), 2005. 
  • Alfonso Gerevini, Alessandro Saetti, Ivan Serina, An experimental study based on Friedman's test of some local search techniques for planningProceedings of the Workshop on Experimental Evaluation and Benchmark of Algorithms for Artificial Intelligence, Ferrara (Italy), 2005. 
  • Alfonso Gerevini, Alessandro Saetti, Ivan Serina, Extending LPG for Planning with Numerical ExpressionsProceedings of the Italian Conference on Intelligent Systems (CISI-04), Perugia (Italy), 2004. 
  • Alfonso Gerevini, Alessandro Saetti, Ivan Serina, Paolo Toninelli, Planning in PDDL2.2 Domains with LPG-TDWorking Notes of the 14th International Conference on Automated Planning & Scheduling (ICAPS-04) - 4th International Planning Competition, Vancouver (Canada), 2004.
  • Alfonso Gerevini, Alessandro Saetti, Ivan Serina, Paolo Toninelli, LPG-TD: a Fully Automated Planner for PDDL2.2 DomainsWorking Notes of the 14th International Conference on Automated Planning & Scheduling (ICAPS-04) - System Demo, Vancouver (Canada), 2004.
  • Alessandro Saetti, Alfonso Gerevini, Ivan Serina, Extending LPG for Numerical PlanningWorking Notes of the 14th International Conference on Automated Planning and Scheduling (ICAPS-04) - Doctoral Consortium, Vancouver (Canada), 2004. 
  • Alfonso Gerevini, Alessandro Saetti, Ivan Serina On Managing Temporal Information for Handling Durative Actions in LPGAI*IA 2003: Advances in Artificial Intelligence, LNAI 2829, Pisa (Italy), 2003.
  • Alessandro Saetti, Managing Temporal Information for Durative Actions in LPGWorking Notes of the 13th International Conference on Automated Planning and Scheduling (ICAPS-03) - Doctoral Consortium, Trento (Italy), 2003. 

Newsletters:

  • Alfonso Gerevini, Alessandro Saetti, Ivan Serina, Automated Planning with Numerical Variables in LPGIntelligenza Artificiale, Vol. 4, Associazione Italiana per l'Intelligenza Artificiale, 2005.
  • Alessandro Saetti, Sergio Spinoni, Temporal Planning Through TDA-Graphs and Local SearchAI*IA-news, Vol. 2, Associazione Italiana per l'Intelligenza Artificiale, 2003. 
  • Alessandro Saetti, Alfonso Gerevini, Ivan Serina, I. Managing Temporal Information for Durative Actions in LPGPLANET News, Vol. 7, European Network of Excellence in AI Planning, 2003.

Technical Reports:

  • Alfonso Gerevini, Alessandro Saetti, Ivan Serina, Planning in Domains with Derived Predicates through Rule-Action Graphs and Local SearchR.T. 2010-04-64, Università degli Studi di Brescia, DII, Brescia, Italia, 2010.
  • Alfonso Gerevini, Alessandro Saetti, Ivan Serina, An Empirical Analysis of Some Heuristic Features for Planning through Local Search and Action GraphsR.T. 2010-01-58, Università degli Studi di Brescia, DII, Brescia, Italia, 2010.
  • Alfonso Gerevini, Patrik Haslum, Derek Long, Alessandro Saetti, Yannis Dimopoulos, Deterministic Planning in the Fifth Planning Competition: PDDL3 and Experimental Evaluation of the PlannersTechnical Report R.T. 2008-02-59, Università degli Studi di Brescia, DEA, Brescia, Italia, 2008. 
  • Alfonso Gerevini, Alessandro Saetti, InLPG: An Interactive Environment for Plan Visualization and GenerationTechnical Report R.T. 2007-03-55, Università degli Studi di Brescia, DEA, Brescia, Italia, 2007. 
  • Alfonso Gerevini, Alessandro Saetti, Ivan Serina, An Approach to Efficient Planning with Numerical Fluents and multi-criteria plan qualityTechnical Report R.T. 2007-01-53, Università degli Studi di Brescia, DEA, Brescia, Italia, 2007. 
  • Alfonso Gerevini, Alessandro Saetti, Ivan Serina, An Approach to Temporal Planning in Domains with Deterministic Exogenous EventsTechnical Report R.T. 2005-06-45, Università degli Studi di Brescia, DEA, Brescia, Italia, 2005. 
  • Alfonso Gerevini, Alessandro Saetti, Ivan Serina, Paolo Toninelli, Planning with Derived Predicates through Rule-Action Graphs and Relaxed-Plan HeuristicsTechnical Report R.T. 2005-01-40, Università degli Studi di Brescia, DEA, Brescia, Italia, 2005.
  • Alfonso Gerevini, Alessandro Saetti, Ivan Serina, On Managing Temporal Information for Handling Durative Actions in LPGTechnical Report R.T. 2003-02-31, Università degli Studi di Brescia, DEA, Brescia, Italia, 2003. 

Proceedings:

  • Proceedings of the Eleventh AI*IA Symposium on Artificial Intelligence (2010). Edited by Alfonso Gerevini & Alessandro Saetti. ISBN: 9-788890-492419.
  • Abstract Booklet of the First AI*IA Doctoral Consortium (2010). Edited by Federico Chesani, Michela Milano & Alessandro Saetti. 
  • Proceedings of the 28th Workshop of the UK Special Interest Group on Planning and Scheduling (2010). Edited by Simone Fratini, Alfonso Gerevini, Derek Long & Alessandro Saetti. ISSN 1368-5708.
  • Abstract Booklet of the Fifth International Planning Competition (2006). Edited by Yannis Dimopoulos, Alfonso Gerevini, Patrik Haslum, Derek Long, Alessandro Saetti.

Thesis:

  • Alessandro Saetti, Automated Planning in Complex Domains: Algorithms and Systems Based on Graphs and Local SearchPh.D. Thesis, Università degli Studi di Brescia, DEA, Brescia, Italia, 2006. (PDF file)

    Abstract: Reasoning about complex information in automated planning is important for representing real-world problems. Unlike classical planning paradigm where strong assumptions are assumed, in real-world domains actions take time and consume resources; the execution of certain actions can occur only during some predefined time windows imposing scheduling constraints, and can indirectly affect high level properties of world states; moreover, the quality of plans generally takes consumption of resources and makespan into account. This thesis proposes some domain-independent techniques based on graphs and local search for automated planning in complex domains. Specifically, the work has three main contributions. Firstly, we propose a rule-based inference system for efficient reasoning about derived predicates during planning; secondly, we describe a new plan representation for reasoning about quantitative information, and some new heuristics for guiding the planning process towards good quality plans; finally, we propose a new approach to planning with temporal features, integrating constraint-based temporal reasoning into a graph-based planning framework. The techniques described in this thesis are implemented in a new planner called LPG-td. LPG-td took part in the 2004 planning competition showing good performance especially in problems formalized by using recent languages enabling the representation of temporal and quantitative information. A large experimental evaluation shows that our approach works well in practice. 

  • Alessandro Saetti, Sergio Spinoni, Algorithms Based on Local Search for Fully-Automated PlanningMaster Thesis, Università degli Studi di Brescia, DEA, Brescia, Italia, 2002.


Alessandro Saetti, August 2019