An algorithm for probabilistic planning
Artificial Intelligence - Special volume on planning and scheduling
Reasoning about noisy sensors and effectors in the situation calculus
Artificial Intelligence
Experiences with an interactive museum tour-guide robot
Artificial Intelligence - Special issue on applications of artificial intelligence
ConGolog, a concurrent programming language based on the situation calculus
Artificial Intelligence
Decision-Theoretic, High-Level Agent Programming in the Situation Calculus
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
On-line execution of cc-Golog plans
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Planning and acting in partially observable stochastic domains
Artificial Intelligence
What is planning in the presence of sensing?
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Reasoning about actions with sensing under qualitative and probabilistic uncertainty
ACM Transactions on Computational Logic (TOCL)
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High-level controllers that operate robots in dynamic, uncertain domains are concerned with at least two reasoning tasks dealing with the effects of noisy sensors and effectors: They have a) to project the effects of a candidate plan and b) to update their beliefs during on-line execution of a plan. In this paper, we show how the pGOLOG framework, which in its original form only accounted for the projection of high-level plans, can be extended to reason about the way the robot's beliefs evolve during the on-line execution of a plan. pGOLOG, an extension of the high-level programming language GOLOG, allows the specification of probabilistic beliefs about the state of the world and the representation of sensors and effectors which have uncertain, probabilistic outcomes. As an application of belief update, we introduce belief-based programs, GOLOG-style programs whose tests appeal to the agent's beliefs at execution time.