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Information about user preferences plays a key role in automated decision making. In many domains it is desirable to assess such preferences in a qualitative rather than quantitative way. In this paper, we propose a qualitative graphical representation of preferences that reflects conditional dependence and independence of preference statements under a(More)
Loosely coupled multi-agent systems are perceived as easier to plan for because they require less coordination between agent sub-plans. In this paper we set out to formalize this intuition. We establish an upper bound on the complexity of multi-agent planning problems that depends exponentially on two parameters quantifying the level of agents' coupling,(More)
In recent years, CP-nets have emerged as a useful tool for supporting preference elici-tation, reasoning, and representation. CP-nets capture and support reasoning with qualitative conditional preference statements, statements that are relatively natural for users to express. In this paper, we extend the CP-nets formalism to handle another class of very(More)
Current heuristic estimators for classical domain-independent planning are usually based on one of four ideas: delete relax-ations, critical paths, abstractions, and, most recently, landmarks. Previously, these different ideas for deriving heuristic functions were largely unconnected. We prove that admissible heuristics based on these ideas are in fact very(More)
Unary operator domains – i.e., domains in which operators have a single effect – arise naturally in many control problems. In its most general form, the problem of STRIPS planning in unary operator domains is known to be as hard as the general STRIPS planning problem – both are PSPACE-complete. However, unary operator domains induce a natural structure,(More)
Many AI tasks, such as product configuration, decision support, and the construction of autonomous agents, involve a process of con-1 strained optimization, that is, optimization of behavior or choices subject to given constraints. In this paper we present an approach for constrained optimization based on a set of hard constraints and a preference ordering(More)
We present a new algorithm for probabilistic planning with no observability. Our algorithm, called Probabilistic-FF, extends the heuristic forward-search machinery of Conformant-FF to problems with probabilistic uncertainty about both the initial state and action effects. Specifically, Probabilistic-FF combines Conformant-FF's techniques with a powerful(More)
We present a fully distributed multi-agent planning algorithm. Our methodology uses distributed constraint satisfaction to coordinate between agents, and local planning to ensure the consistency of these coordination points. To solve the distributed CSP efficiently, we must modify existing methods to take advantage of the structure of the underlying(More)