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Algorithms and Limits for Compact Plan Representations
"... Compact representations of objects is a common concept in computer science. Automated planning can be viewed as a case of this concept: a planning instance is a compact implicit representation of a graph and the problem is to find a path (a plan) in this graph. While the graphs themselves are repres ..."
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Compact representations of objects is a common concept in computer science. Automated planning can be viewed as a case of this concept: a planning instance is a compact implicit representation of a graph and the problem is to find a path (a plan) in this graph. While the graphs themselves are represented compactly as planning instances, the paths are usually represented explicitly as sequences of actions. Some cases are known where the plans always have compact representations, for example, using macros. We show that these results do not extend to the general case, by proving a number of bounds for compact representations of plans under various criteria, like efficient sequential or random access of actions. In addition to this, we show that our results have consequences for what can be gained from reformulating planning into some other problem. As a contrast to this we also prove a number of positive results, demonstrating restricted cases where plans do have useful compact representations, as well as proving that macro plans have favourable access properties. Our results are finally discussed in relation to other relevant contexts. 1.
Knowledge-Based Programs as Plans – The Complexity of Plan Verification –
"... Abstract. Knowledge-based programs (KBPs) are high-level pro-tocols describing the course of action an agent should perform as a function of its knowledge. The use of KBPs for expressing action policies in AI planning has been surprisingly underlooked. Given that to each KBP corresponds an equivalen ..."
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Abstract. Knowledge-based programs (KBPs) are high-level pro-tocols describing the course of action an agent should perform as a function of its knowledge. The use of KBPs for expressing action policies in AI planning has been surprisingly underlooked. Given that to each KBP corresponds an equivalent plan and vice versa, KBPs are typically more succinct than standard plans, but imply more on-line computation time. Here we compare KBPs and standard plans according to succinctness and to the complexity of plan verification. 1
FromMacro PlanstoAutomata Plans
"... Abstract. Macroshavealong-standingroleinplanningasatoolfor representing repeating subsequences of operators. Macros are useful bothforguidingsearchtowardsasolutionandforrepresentingplans compactly. In this paper we introduce automata plans which consist of hierarchies of finite state automata. Autom ..."
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Abstract. Macroshavealong-standingroleinplanningasatoolfor representing repeating subsequences of operators. Macros are useful bothforguidingsearchtowardsasolutionandforrepresentingplans compactly. In this paper we introduce automata plans which consist of hierarchies of finite state automata. Automata plans can be viewed as an extension of macros that enables parametrization and branching. We provide several examples of the utility of automata plans,andprovethatautomataplansarestrictlymoreexpressivethan macro plans. We also prove that automata plans admit polynomialtime sequential access of the operators in the underlying “flat ” plan, and identify a subset of automata plans that admit polynomial-time random access. Finally, we compare automata plans with other representations allowing polynomial-time sequential access. 1
