State-set branching: Leveraging BDDs for heuristic search
نویسندگان
چکیده
منابع مشابه
State-set branching: Leveraging BDDs for heuristic search
In this article, we present a framework called state-set branching that combines symbolic search based on reduced ordered Binary Decision Diagrams (BDDs) with best-first search, such as A* and greedy best-first search. The framework relies on an extension of these algorithms from expanding a single state in each iteration to expanding a set of states. We prove that it is generally sound and opt...
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In this paper we study traditional and enhanced BDDbased exploration procedures capable of handling large planning problems. On the one hand, reachability analysis and model checking have eventually approached AI-Planning. Unfortunately, they typically rely on uninformed blind search. On the other hand, heuristic search and especially lower bound techniques have matured in effectively directing...
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State-set search is state space search when the states being manipulated by the search algorithm are sets of states from some underlying state space. State-set search arises commonly in planning and abstraction systems, but this paper provides the first formal, general analysis of state-set search. We show that the state-set distance computed by planning systems is different than that computed ...
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The idea of using BDDs for optimal sequential planning is to reduce the memory requirements for the state sets as problem sizes increase. State variables are encoded binary and ordered along their causal graph dependencies. Sets of planning states are represented in form of Boolean functions, and actions are formalized as transition relations. This allows to compute the successor state set, whi...
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ژورنال
عنوان ژورنال: Artificial Intelligence
سال: 2008
ISSN: 0004-3702
DOI: 10.1016/j.artint.2007.05.009