The complexity of propositional linear temporal logics
Journal of the ACM (JACM)
Artificial intelligence and mathematical theory of computation
Conditional nonlinear planning
Proceedings of the first international conference on Artificial intelligence planning systems
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Open World Planning in the Situation Calculus
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Conformant Planning via Model Checking
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
Planning for contingencies: a decision-based approach
Journal of Artificial Intelligence Research
Constructing conditional plans by a theorem-prover
Journal of Artificial Intelligence Research
Planning as model checking for extended goals in non-deterministic domains
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
What is planning in the presence of sensing?
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
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The "planning as satisfiability" paradigm, which reduces solving a planning problem P to the search of a model of a logical description of P, relies on the assumption that the agent has complete knowledge and control over the world. This work faces the problem of planning in the presence of incomplete information and/or exogenous events, still keeping inside the "planning as satisfiability" paradigm, in the context of linear time logic.We give a logical characterization of a "conditioned model", which represents a plan solving a given problem together with a set of "conditions" that guarantee its executability. During execution, conditions have to be checked by means of sensing actions. When a condition turns out to be false, a different "conditioned plan" must be considered. A whole conditional plan is represented by a set of conditioned models. The interest of splitting a conditional plan into significant sub-parts is due to the heavy computational complexity of conditional planning.The paper presents an extension of the standard tableau calculus for linear time logic, allowing one to extract from a single open branch a conditioned model of the initial set of formulae, i.e. a partial description of a model and a set of conditions U guaranteeing its "executability". As can be expected, if U is required to be minimal, the analysis of a single branch is not sufficient. We show how a global view on the whole tableau can be used to prune U from redundant conditions. In any case, if the calculus is to be used with the aim of producing the whole conditional plan off-line, a complete tableau must be built. On the other hand, a single conditioned model can be used when planning and execution (with sensing actions) are intermingled. In that case, the requirement for minimality can reasonably be relaxed.