Research on Decreasing Observation Variables for Strong Planning under Partial Observation

How to decrease the observation variables for strong planning under partial observation is explored. Beginning from a domain under no observation,add necessary observation variables gradually to get a minimal set of observation variables necessary.Two methods are presented to decrease observation variables.With the former, when any of the two distinct states of the domain can be distinguished by an observation variable,this algorithm can find a minimal set of observation variables necessary for the execution of a plan.With the latter,when there are states that can't be distinguished by only one observation variable,this algorithm can find a set of observation variables as small as possible which are necessary for the execution of a plan.

[1]  Jigui Sun,et al.  Counting Models using Extension Rules , 2007, AAAI.

[2]  Piergiorgio Bertoli,et al.  Interleaving Execution and Planning for Nondeterministic, Partially Observable Domains , 2004, ECAI.

[3]  Andreas Herzig,et al.  Action representation and partially observable planning using epistemic logic , 2003, IJCAI.

[4]  Paolo Traverso,et al.  Strong Planning in Non-Deterministic Domains Via Model Checking , 1998, AIPS.

[5]  Jiang Yun-Fei,et al.  Planning with Domain Constraints Based on Model-Checking , 2004 .

[6]  Piergiorgio Bertoli,et al.  Conformant planning via symbolic model checking and heuristic search , 2004, Artif. Intell..

[7]  Marco Roveri,et al.  Conformant Planning via Symbolic Model Checking , 2000, J. Artif. Intell. Res..

[8]  Carmel Domshlak,et al.  Fast Probabilistic Planning through Weighted Model Counting , 2006, ICAPS.

[9]  Blai Bonet,et al.  Planning with Incomplete Information as Heuristic Search in Belief Space , 2000, AIPS.

[10]  Piergiorgio Bertoli,et al.  Strong planning under partial observability , 2006, Artif. Intell..

[11]  Craig Boutilier,et al.  Vector-space Analysis of Belief-state Approximation for POMDPs , 2001, UAI.

[12]  Wei Huang,et al.  Observation Reduction for Strong Plans , 2007, IJCAI.

[13]  Daniel S. Weld.  Recent Advances in AI Planning , 1999, AI Mag..

[14]  Piergiorgio Bertoli,et al.  Planning in Nondeterministic Domains under Partial Observability via Symbolic Model Checking , 2001, IJCAI.

[15]  Ronen I. Brafman,et al.  Conformant planning via heuristic forward search: A new approach , 2004, Artif. Intell..

[16]  Piergiorgio Bertoli,et al.  Heuristic Search + Symbolic Model Checking = Efficient Conformant Planning , 2001, IJCAI.

[17]  R. Brafman,et al.  Contingent Planning via Heuristic Forward Search witn Implicit Belief States , 2005, ICAPS.

[18]  Jigui Sun,et al.  Extension or Resolution: A Novel Approach for Reasoning in Possibilistic Logic , 2007, Analysis and Design of Intelligent Systems using Soft Computing Techniques.