In this paper, we bring techniques from operations research to bear on the problem of choosing optimal actions in partially observable stochastic domains. We begin by introducing the theory of Markov decision processes (mdps) and partially observable mdps (pomdps). We then outline a novel algorithm for solving pomdps offline and show how, in some cases, a finite-memory controller can be extracted from the solution to a pomdp. We conclude with a discussion of how our approach relates to previous work, the complexity of finding exact solutions to pomdps, and of some possibilities for finding approximate solutions.

If an agent does not have complete information about the world-state, it must reason about alternative possible states of the world and consider whether any of its actions can reduce the uncertainty. Agents controlled by a contingent planner seek to generate a robust plan, that accounts for and handles all eventualities, in advance of execution. Thus a contingent plan may include sensing actions which gather information that is later used to select between different plan branches. Unfortunately, previous contingent planners suffered defects such as confused semantics, incompleteness, and inefficiency. In this paper we describe SGP, a descendant of Graphplan that solves contingent planning problems. SGP distinguishes between actions that sense the value of an unknown proposition from those that change its value. SGP does not suffer from the forms of incompleteness displayed by CNLP and Cassandra. Furthermore, SGP is relatively fast. 1 Introduction Classical planners make the unrealisti...

The research on conditional planning rejects the assumptions that there is no uncertainty or incompleteness of knowledge with respect to the state and changes of the system the plans operate on. Without these assumptions the sequences of operations that achieve the goals depend on the initial state and the outcomes of nondeterministic changes in the system. This setting raises the questions of how to represent the plans and how to perform plan search. The answers are quite different from those in the simpler classical framework. In this paper, we approach conditional planning from a new viewpoint that is motivated by the use of satisfiability algorithms in classical planning. Translating conditional planning to formulae in the propositional logic is not feasible because of inherent computational limitations. Instead, we translate conditional planning to quantified Boolean formulae. We discuss three formalizations of conditional planning as quantified Boolean formulae, and pr...

yQSS Group Inc. zQSS Group Inc. xRIACS experiment is assigned a scientific value). Different ob-servations and experiments take differing amounts of time and consume differing amounts of power and data storage.There are, in general, a number of constraints that govern the rovers activities: ffl There are time, power, data storage, and posi-tioning constraints for performing different activities. Time constraints often result from illuminationrequirement--that is, experiments may require that a target rock or sample be illuminated with a certain in-tensity, or from a certain angle.

In this paper we present a new approach to the problem of planning with incomplete information and sensing. Our approach is based on a higher level, "knowledge-based," representation of the planner's knowledge and of the domain actions. In particular, in our approach we use a set of formulae from a first-order modal logic of knowledge to represent the planner's incomplete knowledge state. Actions are then represented as updates to this collection of formulae. Hence, actions are being modelled in terms of how they modify the knowledge state of the planner rather than in terms of how they modify the physical world. We have constructed a planner to utilize this representation and we use it to show that on many common problems this more abstract representation is perfectly adequate for solving the planning problem, and that in fact it scales better and supports features that make it applicable to much richer domains and problems.

In Part I of this series of papers, we have proposed a new logic-based planning language, called K. This language facilitates the description of transitions between states of knowledge and it 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. In the present Part II, we describe the DLV planning system, which implements K on top of the disjunctive logic programming system DLV. This novel planning system allows for solving hard planning problems, including secure planning under incomplete initial states (often called conformant planning in the literature), which cannot be solved at all by other logic-based planning systems such as traditional satisfiability planners. We present a detailed comparison of the system to several state-of-the-art conformant planning systems, both at the level of system features and on benchmark problems. Our results indicate that, thanks to the power of knowledge-state problem encoding, the DLV system is competitive even with special purpose conformant planning systems, and it often supplies a more natural and simple representation of the planning problems.

To build effective planning systems, it is crucial to find the right level of representation: too impoverished, and important actions and goals are impossible to express; too expressive, and planning becomes intractable.

In presence of incomplete information about the world we need to distinguish between the state of the world and the state of the agent’s knowledge about the world. In such a case the agent may need to have at its disposal sensing actions that change its state of knowledge about the world and may need to construct more general plans consisting of sensing actions and conditional statements to achieve its goal. In this paper we first develop a high-level action description language that allows specification of sensing actions and their effects in its domain description and allows queries with conditional plans. We give provably correct translations of domain description in our language to axioms in first-order logic, and relate our formulation to several earlier formulations in the literature. We then analyze the state space of our formulation and develop several sound approximations that have much smaller state spaces. Finally we define regression of knowledge formulas over conditional plans,

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We are concerned with the implications and interactions of three common expressive extensions to classical planning: conditional plans, context-dependent actions, and nondeterministic action outcomes. All of these extensions have appeared in recent work, sometimes in conjunction, but the semantics of the combination has not been fully explored. As we have argued in previous work, providing a coherent semantics for conditional planning with context-dependent actions requires that the planner's information state be modelled separately from the world state. In this paper, we present a new planning language, WCPL, encompassing these extensions. The semantics of WCPL includes an explicit treatment of the planner's information state as knowledge, as opposed to some form of context labelling. In addition to clarifying and unifying a disparate set of results from earlier work, we extend that work: WCPL handles both conditional and fail-safe plans for an action representation including both context-dependent and nondeterministic actions.