Prof. Alessandro Saetti
Dipartimento di Ingegneria dell'Informazione
Facoltà di Ingegneria, Università degli Studi di Brescia
Via Branze 38, I-25123 Brescia, Italy.
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 Problems, Journal 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 Satisfiability, AI 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 Approach, Journal 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 Search, Annals 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 Closure, Artificial 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 Graphs, Fundamenta 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 Planners, Artificial 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 Quality, Artificial 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 Events, Journal 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 LPG, Journal 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.