The formulation of planning as heuristic search with heuristics derived from problem representations has turned out to be a fruitful approach for classical planning. In this paper, we pursue a similar idea in the context planning with incomplete information. Planning with incomplete information can be formulated as a problem of search in belief space, where belief states can be either sets of states or more generally probability distribution over states. While the formulation (as the formulation of classical planning as heuristic search) is not particularly novel, the contribution of this paper is to make it explicit, to test it over a number of domains, and to extend it to tasks like planning with sensing where the standard search algorithms do not apply. The resulting planner appears to be competitive with the most recent conformant and contingent planners (e.g., cgp, sgp, and cmbp) while at the same time is more general as it can handle probabilistic actions and se...

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The Model Based Planner (MBP) is a system for planning in non-deterministic domains. It can generate plans automatically to solve various planning problems, like conformant planning, planning under partial observability, and planning for temporally extended goals. Moreover, MBP can validate plans, and offers a variety of simulation functionalities for plans and domains. MBP is based on Symbolic Model Checking techniques, and Binary Decision Diagrams (BDDs), that provide a practical solution to the problem of dealing with the large size of realistic planning problems. Experimental analysis in the course of the last years has shown MBP to be state-of-the-art in planning for nondeterministic domains. The demo aims at showing MBP’s array of functionalities for plan generation, validation and simulation over an increasingly complex navigation problem.

We study an automata-theoretic approach to planning for temporally extended goals. Specifically, we devise techniques based on nonemptiness of Büchi automata on infinite words, to synthesize sequential and conditional plans in a generalized setting in which we have that: goals are general temporal properties of desired execution; dynamic systems are represented by finite transition systems; incomplete information on the initial situation is allowed; and states are only partially observable. We prove that the techniques proposed are optimal wrt the worst case complexity of the problem. Thanks to the scalability of the nonemptiness algorithms, the techniques presented here promise to be applicable to fairly large systems, notwithstanding the intrinsic complexity of the problem.

This paper invents symbolic pattern databases (SPDB) to combine two influencing aspects for recent progress in domain-independent action planning, namely heuristic search and model checking. SPDBs are off-line computed dictionaries, generated in symbolic backward traversals of automatically inferred planning space abstractions. The entries of SPDBs serve as heuristic estimates to accelerate explicit and symbolic, approximate and optimal heuristic search planners. Selected experiments highlight that the symbolic representation yields much larger and more accurate pattern databases than the ones generated with explicit methods.

We describe a forward reasoning planner for open worlds that uses domain specific information for pruning its search space, as suggested by (Bacchus & Kabanza 1996; 2000). The planner is written in the situation calculus-based programming language GOLOG, and it uses a situation calculus axiomatization of the application domain. Given a sentence oe to prove, the planner regresses it to an equivalent sentence oe 0 about the initial situation, then invokes a theorem prover to determine whether the initial database entails oe 0 and hence oe. We describe two approaches to this theorem proving task, one based on compiling the initial database to prime implicate form, the other based on Relsat, a Davis/Putnam-based procedure. Finally, we report on our experiments with open world planning based on both these approaches to the theorem proving task.

Planning as satisfiability is a very efficient technique for classical planning, i.e., for planning domains in which both the effects of actions and the initial state are completely specified. In this paper we present C-SAT, a SAT-based procedure capable of dealing with planning domains having incomplete information about the initial state, and whose underlying transition system is specified using the highly expressive action language C. Thus, C-SAT allows for planning in domains involving (i) actions which can be executed concurrently; (ii) (ramification and qualification) constraints affecting the effects of actions; and (iii) nondeterminism in the initial state and in the effects of actions. We first prove the correctness and the completeness of C-SAT, discuss some optimizations, and then we present C-PLAN, a system based on C-SAT. C-PLAN works on any C planning problem, but some optimizations have not been fully implemented yet. Nevertheless, the experimental analysis shows that SAT-based approaches to planning with incomplete information are viable, at least in the case of problems with a high degree of parallelism.

Several realistic non-deterministic planning domains require plans that encode iterative trial-and-error strategies, e.g., "pick up a block until succeed". In such domains, a certain effect (e.g., action success) might never be guaranteed a priori of execution and, in principle, iterative plans might loop forever. Here, the planner should generate iterative plans whose executions always have a possibility of terminating and, when they do, they are guaranteed to achieve the goal. In this paper, we define the notion of strong cyclic plan, which formalizes in temporal logic the above informal requirements for iterative plans, define a planning algorithm based on model-checking techniques, and prove that the algorithm is guaranteed to return strong cyclic plans when they exist or to terminate with failure when they do not. We show how this approach can be extended to formalize plans that are guaranteed to achieve the goal and do not involve iterations (strong plans) and plans that have a possibility (but are not guaranteed) to achieve the goal (weak plans). The results presented in this paper constitute a formal account for "planning via model checking" in non-deterministic domains, which has never been provided before.

We present a logic programming based conditional planner that is capable of generating both conditional and sequential conformant plans in the presence of sensing actions and incomplete information. We prove the correctness of our implementation and show that our planner is complete with respect to the 0-approximation of sensing actions and the class of conditional plans considered in this paper which is large enough to cover conditional plans with bounded length and branching factor. Finally, we present some preliminary experimental results and discuss further enhancements to the program.

. We propose a new logic-based planning language, called K. Transitions between states of knowledge can be described in K, and the language is well suited for planning under incomplete knowledge. Nonetheless, K also supports the representation of transitions between states of the world (i.e., states of complete knowledge) as a special case, proving to be very flexible. A planning system supporting K is implemented on top of the disjunctive logic programming system DLV. This novel system allows for solving hard planning problems, including secure planning under incomplete initial states, which cannot be solved at all by other logic-based planning systems such as traditional satisfiability planners. 1 Introduction The need for modeling the behavior of robots in a formal way led to the definition of logic-based languages for reasoning about actions and action planning, such as [24, 8, 15, 10, 34, 11, 19, 12, 14]. These languages allow us to specify planning problems of the form ...