Goal Achievement in Partially Known, Partially Observable Domains

We present a decision making algorithm for agents that act in partially observable domains which they do not know fully. Making intelligent choices in such domains is very difficult because actions' effects may not be known a priori (partially known domain), and features may not always be visible (partially observable domain). Nonetheless, we show that an efficient solution is achievable in STRIPS domains by using traditional planning methods. This solution interleaves planning and execution carefully. Computing each plan takes time that is linear in the planning time for the fully observable, fully known domain. The number of actions that it executes is bounded by a polynomial in the length of the optimal plan in the fully observable, fully known domain. Our theoretical results and preliminary experiments demonstrate the effectiveness of the algorithm.

• Type: Conference
• Year: 2006
• Where: AIPS
• Authors: Allen Chang, Eyal Amir
• Keywords: AIPS 2006, Artificial Intelligence, Decision Making Algorithm, Observable Domain, Solution Interleaves Planning