Conformant planning is the problem of finding a sequence of actions for achieving a goal in the presence of uncertainty in the initial state or action effects. The problem has been approached as a path-finding problem in belief space where good belief representations and heuristics are critical for scaling up. In this work, a different formulation is introduced for conformant problems with deterministic actions where they are automatically converted into classical ones and solved by an off-the-shelf classical planner. The translation maps literals L and sets of assumptions t about the initial situation, into new literals KL/t that represent that L must be true if t is initially true. We lay out a general translation scheme that is sound and establish the conditions under which the translation is also complete. We show that the complexity of the complete translation is exponential in a parameter of the problem called the conformant width, which for most benchmarks is bounded. The planner based on this translation exhibits good performance in comparison with existing planners, and is the basis for T0, the best performing planner in the Conformant Track of the 2006 International Planning Competition. 1.

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Abstract. When several agents operate in a common environment, their plans may interfere so that the predicted outcome of each plan may be altered, even if it is composed of deterministic actions, only. Most of the multi-agent planning frameworks either view the actions of the other agents as exogeneous events or consider goal sharing cooperative agents. In this paper, we depart from such frameworks and extend the well-known single agent framework for classical planning to a multi-agent one. Focusing on the two agents case, we show how valuable plans can be characterized using game-theoretic notions, especially Nash equilibrium. 1

Researchers have developed a huge number of algorithms to solve classical planning problems. We provide a way to use these algorithms, unmodified, to generate strong-cyclic solutions in fully-observable nondeterministic planning domains. Our experiments show that when using our technique with FF and SGPlan (two well-known classical planners), its performance compares quite favorably to that of MBP, one of the best-known planners for nondeterministic planning problems.

Engineering complete planning domain descriptions is often very costly because of human-error or lack of domain knowledge. While many have studied knowledge acquisition, relatively few have studied the synthesis of plans when the domain model is incomplete (i.e., actions have incomplete preconditions or effects). Prior work has evaluated the correctness of plans synthesized by disregarding such incomplete features, but not how to synthesize plans by reasoning about the incompleteness. In this work, we describe several techniques for reasoning with the action incompleteness to make plans robust, as measured by the number of incomplete domain interpretations under which a plan succeeds. Among the techniques, we show that representing explanations of plan failure with prime implicants provides a natural approach to comparing plans by counting prime implicants (diagnoses) instead of domain interpretations (i.e., model counting) – leading to better scalability and higher quality plans. We present and empirically evaluate a forward heuristic search planner, called DeFAULT, that synthesizes plans by propagating information about faults due to incompleteness both within the state space and the relaxed planning space by using intuitions from model based diagnosis and assumption-based truth maintenance systems. We also provide a translation from incomplete planning domains to conformant probabilistic planning, where action incompleteness is represented by state incompleteness. We compare DeFAULT with a control planner that uses the FF heuristic (measuring plan length and ignoring incompleteness), and with the conformant probabilistic planner POND. The results show that DeFAULT i) scales better than POND, ii) finds better solutions than a planner using the FF heuristic, iii) scales best and finds its best quality solutions when counting prime implicants rather than models. 1

Planning graphs have been shown to be a rich source of heuristic information for many kinds of planners. In many cases, planners must compute a planning graph for each element of a set of states, and the naive technique enumerates the graphs individually. This is equivalent to solving an all-pairs shortest path problem by iterating a single-source algorithm over each source. We introduce a structure, the state agnostic planning graph, that directly solves the all-pairs problem for the relaxation introduced by planning graphs. The technique can also be characterized as exploiting the overlap present in sets of planning graphs. For the purpose of exposition, we first present the technique in deterministic planning. A more prominent application of this technique is in belief state space planning, where an optimization to exploit state overlap between belief states results in drastically improved theoretical complexity. We describe another extension in probabilistic planning that uses common action outcome uncertainty to further improve theoretical complexity. Our experimental evaluation (using many existing International Planning Competition problems) quantifies each of these performance boosts, and demonstrates that heuristic belief state

In dynamic domains, the state of the world may change in unexpected ways during the generation or execution of plans. Regardless of the cause of such changes, they raise the question of whether they interfere with ongoing planning efforts. Unexpected changes during plan generation may invalidate the current planning effort, while discrepancies between expected and actual state of the world during execution may render the executing plan invalid or sub-optimal, with respect to previously identified planning objectives. In this thesis we develop a general monitoring technique that can be used during both plan generation and plan execution to determine the relevance of unexpected changes and which supports recovery. This way, time intensive replanning from scratch in the new and unexpected state can often be avoided. The technique can be applied to a variety of objectives, including monitoring the optimality of plans, rather then just their validity. Intuitively, the technique operates in two steps: during planning the plan is annotated with additional information that is relevant to the achievement of the objective; then, when an unexpected change occurs, this information is used to determine the relevance of the discrepancy with respect to the objective.

Even under polynomial restrictions on plan length, conformant planning remains a very hard computational problem as plan verification itself can take exponential time. This heavy price cannot be avoided in general although in many cases conformant plans are verifiable efficiently by means of simple forms of disjunctive inference. We report an efficient but incomplete planner capable of solving non-trivial problems quickly. In this work, we show that this is possible by mapping conformant into classical problems that are then solved by an off-the-shelf classical planner. The formulation is sound as the classical plans obtained are all conformant, but it is incomplete as the inverse relation does not always hold. Atoms L/Xi that represent conditional beliefs ’if Xi then L’ are introduced in the classical encoding and combined with suitable actions when certain invariants are verified. Empirical results over a wide variety of problems illustrate the power of the approach. We propose extensions to this formulation.

In this talk, I will detail the development of CpA(H), a competitive conformant planner,

In this paper, we introduce a new approach to conformant planning via classical planners. We view a conformant planning problem as a set of classical planning problems, called sub-problems, and solve it using a generate-and-complete algorithm. Key to this algorithm is a procedure which takes a solution of a sub-problem and generates a solution for other sub-problems. We implement this algorithm in a new planner, called CPCL and evaluate it empirically against state-of-theart conformant planners using various benchmarks. The experimental results show that CPCL is superior to other planners in most benchmarks, both in performance and in scalability.