Recent Advances in AI Planning

Context dependent effects in temporal planning.- Information gathering plans with sensing actions.- What does a planner need to know about execution?.- Planning and chemical plant operating procedure synthesis: A case study.- Task planning and partial order planning: A domain transformation approach.- Expressing transformations of structured reactive plans.- Fast probabilistic plan debugging.- Quantification in generative refinement planning.- Modal tableaux for reasoning about actions and plans.- A time and resource problem for planning architectures.- Planning via model checking: A decision procedure for AR.- Possibilistic planning: Representation and complexity.- Planning with sensing for a mobile robot.- Encoding planning problems in nonmonotonic logic programs.- An argument for a hybrid HTN/operator-based approach to planning.- Natural hierarchical planning using operator decomposition.- Planning, learning, and executing in autonomous systems.- Combining the expressivity of UCPOP with the efficiency of Graphplan.- Bounded parameter Markov decision processes.- Planning and search techniques for intelligent behaviour of battlefield entities.- Understanding and extending Graphplan.- Extending planning graphs to an ADL subset.- Breaking security protocols as an AI planning problem.- Time-oriented skeletal plans: Support to design and execution.- SINERGY: A linear planner based on genetic programming.- A case study on mergeability of cases with a partial-order planner.- Ignoring irrelevant facts and operators in plan generation.- A tabu search strategy to solve scheduling problems with deadlines and complex metric constraints.- Contingency selection in plan generation.- Approaching the plans are programs paradigm using transaction logic.- Event calculus planning revisited.- Constraint based reactive rescheduling in a stochastic environment.- Plan-refinement strategies and search-space size.- Replanning using hierarchical task network and operator-based planning.- BI-POMDP: Bounded, incremental partially-observable Markov-model planning.