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AI
1998
Springer

The Logical Foundations of Goal-Regression Planning in Autonomous Agents

8 years 11 months ago
The Logical Foundations of Goal-Regression Planning in Autonomous Agents
This paper addresses the logical foundations of goal-regression planning in autonomous rational agents. It focuses mainly on three problems. The first is that goals and subgoals will often be conjunctions, and to apply goal-regression planning to a conjunction we usually have to plan separately for the conjuncts and then combine the resulting subplans. A logical problem arises from the fact that the subplans may destructively interfere with each other. This problem has been partially solved in the AI literature (e.g., in SNLP and UCPOP), but the solutions proposed there work only when a restrictive assumption is satisfied. This assumption pertains to the computability of threats. It is argued that this assumption may fail for an autonomous rational agent operating in a complex environment. Relaxing this assumption leads to a theory of defeasible planning. The theory is formulated precisely and an implementation in the OSCAR architecture is discussed. The second problem is that goal-re...
John L. Pollock
Added 21 Dec 2010
Updated 21 Dec 2010
Type Journal
Year 1998
Where AI
Authors John L. Pollock
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