Titlebook: Recent Advances in AI Planning; 4th European Confere Sam Steel,Rachid Alami Conference proceedings 1997 Springer-Verlag Berlin Heidelberg 1

Merited

dyspareunia

Expressing transformations of structured reactive plans, partly unknown environment. XFRML allows XFRM to anticipate and forestall many common flaws in autonomous robot behavior that cannot be dealt with by other planning representations. In order to diagnose execution failures in projected execution scenarios, XFRM has to infer whether or not particular

Cumulus

Fast probabilistic plan debugging, the world. These more realistic models together with the robots‘ lack of, and uncertainty in, information, however, yield so many ways the world might change as plan gets executed that the prediction of the . of something happening gets infeasible. In this paper, we discuss FPPD (Fast Probabilistic

MANIA

Quantification in generative refinement planning, We add full quantification to a generative refinement planning framework, not by expanding to a . [9], but by Skolemizing. We apply our results to causal link planning which leads to a new conflict resolution strategy, a notion called . the ..

dominant

Modal tableaux for reasoning about actions and plans,ropositional dynamic logic, wherein we solve the frame problem by introducing the notion of dependence as a weak causal connection between actions and atoms. The tableau procedure is sound and complete for an important fragment of our language, within which all standard problems of reasoning about a

Latency

A time and resource problem for planning architectures,m (.), in which resource reasoning is seen as integrated with temporal reasoning. Two propagation techniques are introduced that reason about resource constraints in the . framework. The Profile Propagation technique, similar to time-tabling techniques, considers resource utilization in single insta

Lacunar-Stroke

Planning via model checking: A decision procedure for AR,ism which is able to handle, among other things, ramifications and non-deterministic effects. We define a decision procedure for planning in . which is based on “symbolic model checking”, a technique which has been successfully applied in hardware and software verification. The decision procedure al

EVADE

Possibilistic planning: Representation and complexity,wn and the actions have graded nondeterministic effects, some being more possible (normal) than the others. The uncertainty on states and effects of actions is represented by possibility distributions. The paper first recalls the essence of possibilitic planning concerning the representational aspec

向前变椭圆

Planning with sensing for a mobile robot,cally, we present a logical framework for representing dynamic systems based on description logics, which allows for the formalization of sensing actions. We address the generation of conditional plans by defining a suitable reasoning method in which a plan is extracted from a constructive proof of

Goblet-Cells