Fast Downward is a classical planning system based on heuristic search. It can deal with general deterministic planning problems encoded in the propositional fragment of PDDL2.2, including advanced features like ADL conditions and effects and derived predicates (axioms). Like other well-known planners such as HSP and FF, Fast Downward is a progression planner, searching the space of world states of a planning task in the forward direction. However, unlike other PDDL planning systems, Fast Downward does not use the propositional PDDL representation of a planning task directly. Instead, the input is first translated into an alternative representation called multivalued planning tasks, which makes many of the implicit constraints of a propositional planning task explicit. Exploiting this alternative representation, Fast Downward uses hierarchical decompositions of planning tasks for computing its heuristic function, called the causal graph heuristic, which is very different from traditional HSP-like heuristics based on ignoring negative interactions of operators. In this article, we give a full account of Fast Downward’s approach to solving multi-valued planning tasks. We extend our earlier discussion of the causal graph heuristic to tasks involving

Abstract not found

In this article, we work towards the goal of developing agents that can learn to act in complex worlds. We develop a a new probabilistic planning rule representation to compactly model model noisy, nondeterministic action effects and show how these rules can be effectively learned. Through experiments in simple planning domains and a 3D simulated blocks world with realistic physics, we demonstrate that this learning algorithm allows agents to effectively model world dynamics.

LAMA is a classical planning system based on heuristic forward search. Its core feature is the use of a pseudo-heuristic derived from landmarks, propositional formulas that must be true in every solution of a planning task. LAMA builds on the Fast Downward planning system, using finite-domain rather than binary state variables and multi-heuristic search. The latter is employed to combine the landmark heuristic with a variant of the well-known FF heuristic. Both heuristics are cost-sensitive, focusing on high-quality solutions in the case where actions have non-uniform cost. A weighted A ∗ search is used with iteratively decreasing weights, so that the planner continues to search for plans of better quality until the search is terminated. LAMA showed best performance among all planners in the sequential satisficing track of the International Planning Competition 2008. In this paper we present the system in detail and investigate which features of LAMA are crucial for its performance. We present individual results for some of the domains used at the competition, demonstrating good and bad cases for the techniques implemented in LAMA. Overall, we find that using landmarks improves performance, whereas the incorporation of action costs into the heuristic estimators proves not to be beneficial. We show that in some domains a search that ignores cost solves far more problems, raising the question of how to deal with action costs more effectively in the future. The iterated weighted A ∗ search greatly improves results, and shows synergy effects with the use of landmarks. 1.

Planning with preferences involves not only finding a plan that achieves the goal, it requires finding a preferred plan that achieves the goal, where preferences over plans are specified as part of the planner’s input. In this paper we provide a technique for accomplishing this objective. Our technique can deal with a rich class of preferences, including so-called temporally extended preferences (TEPs). Unlike simple preferences which express desired properties of the final state achieved by a plan, TEPs can express desired properties of the entire sequence of states traversed by a plan, allowing the user to express a much richer set of preferences. Our technique involves converting a planning problem with TEPs into an equivalent planning problem containing only simple preferences. This conversion is accompished by augmenting the inputed planning domain with a new set of predicates and actions for updating these predicates. We then provide a collection of new heuristics and a specialized search algorithm that can guide the planner towards preferred plans. Under some fairly general conditions our method is able to find a most preferred plan—i.e., an optimal plan. It can accomplish this without having to resort to admissible heuristics, which often perform poorly in practice. Nor does our technique require an assumption of restricted plan length or make-span. We have implemented our approach in the HPLAN-P planning system and used it to compete in the 5th International Planning Competition, where it achieved distinguished performance in the Qualitative Preferences track.

We present an algorithm for learning a model of the effects of actions in noisy stochastic worlds. We consider learning in a 3D simulated blocks world with realistic physics. To model this world, we develop a planning representation with explicit mechanisms for expressing object reference and noise. We then present a learning algorithm that can create rules while also learning derived predicates, and evaluate this algorithm in the blocks world simulator, demonstrating that we can learn rules that effectively model the world dynamics.

Abstract: Modern information systems are becoming more and more socio-technical systems, namely systems composed of human (social) agents and software (technical) systems operating together in a common environment. The structure of such systems has to evolve dynamically in response to the changes of the environment. When new requirements are introduced, when an actor leaves the system or when a new actor comes, the socio-technical structure needs to be redesigned and revised. In this paper, an approach to dynamic reconfiguration of a socio-technical system structure in response to internal or external changes is proposed. The approach is based on planning techniques for generating possible alternative configurations, and local strategies for their evaluation. The reconfiguration mechanism is presented, which makes the socio-technical system self-configuring, and the approach is discussed and analyzed on a simple case study.

The treatment of exogenous events in planning is practically important in many realworld 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. 1.

Abstract. The quest for designing secure and trusted software has led to refined Software Engineering methodologies that rely on tools to support the design process. Automated reasoning mechanisms for requirements and software verification are by now a well-accepted part of the design process, and model driven architectures support the automation of the refinement process. We claim that we can further push the envelope towards the automatic exploration and selection among design alternatives and show that this is concretely possible for Secure Tropos, a requirements engineering methodology that addresses security and trust concerns. In Secure Tropos, a design consists of a network of actors (agents, positions or roles) with delegation/permission dependencies among them. Accordingly, the generation of design alternatives can be accomplished by a planner which is given as input a set of actors and goals and generates alternative multiagent plans to fulfill all given goals. We validate our claim with a case study using a state-of-the-art planner. 1

Abstract. At the early stages of the cooperative information system development one of the major problems is to explore the space of alternative ways of assignment and delegations of goals among system actors. The exploration process should be guided by a number of criteria to determine whether the adopted alternative is good-enough. This paper frames the problem of designing actor dependency networks as a multi-agent planning problem and adopts an off-the-shelf planner to offer a tool (P-Tool) that generates alternative actor dependency networks, and evaluates them in terms of metrics derived from Game Theory literature. As well, we offer preliminary experimental results on the scalability of the approach. 1