Showing papers on "Qualification problem published in 2004"
Proceedings Article•
1 Jan 2004TL;DR: This work deals with the qualification problem by adding a feature construct on top of the situation calculus, which is a basis for first-order automated induction prover construction, in a fully automated way.
Abstract: A well-known way to reason about infinite-state systems is mathematical induction However, describing such systems for a first-order automated induction prover is a complex task requiring a specialist To handle this complexity we propose to use the situation calculus as a basis We deal with the qualification problem by adding a feature construct on top of the situation calculus We report on a preliminary case study demonstrating the use of our fully automated tool
1 citations
TL;DR: A high-level programming method is presented to combine sensing actions and explanations for an action failure in the agent programming language FLUX, which builds on the general action representation formalism of the Fluent Calculus.
Abstract: Planning agents in real-world environments have to face the Qualification Problem, i.e., the failure of an action execution due to unexpected circumstances. Sensing actions are used to derive additional state knowledge. We present a high-level programming method to combine these two approaches in the agent programming language FLUX, which builds on the general action representation formalism of the Fluent Calculus. It is shown how this combination allows for an efficient reasoning about the causes of unexpected action failures. The explanations for an action failure help an agent to recover from unexpectedly failed plans.