A model for reasoning about persistence and causation
Computational Intelligence
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
Artificial intelligence and mathematical theory of computation
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
Using abstractions for decision-theoretic planning with time constraints
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
An algorithm for probabilistic planning
Artificial Intelligence - Special volume on planning and scheduling
Logic for Problem Solving
Planning and Acting in Partially Observable Stochastic Domains
Planning and Acting in Partially Observable Stochastic Domains
Logic programming for robot control
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Universal plans for reactive robots in unpredictable environments
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
Process-oriented planning and average-reward optimality
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Exploiting structure in policy construction
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Reasoning about noisy sensors in the situation calculus
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
What is planning in the presence of sensing?
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Computing optimal policies for partially observable decision processes using compact representations
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Planning with deadlines in stochastic domains
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Efficient decision-theoretic planning: techniques and empirical analysis
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Probabilistic complex actions in GOLOG
Fundamenta Informaticae
Probabilistic Complex Actions in GOLOG
Fundamenta Informaticae - The 1st International Workshop on Knowledge Representation and Approximate Reasoning (KR&AR)
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This paper shows how we can combine logical representations of actions and decision theory in such a manner that seems natural for both. In partitular we assume an axiomatization of the domain in terms of situation calculus, using what is essentially Reiter's solution to the frame problem, in terms of the completion of the axioms defining the state change. Uncertainty is handled in terms of the independent choice logic, which allows for independent choices and a logic program that gives the consequences of the choices. As part of the consequences are a specification of the utility of (final) states. The robot adopts robot plans, similar to the GOLOG programming language. Within this logic, we can define the expected utility of a conditional plan, based on the axiomadzation of the actions, the uncertainty and the utility. The 'planning' problem is to find the plan with the highest expected utility. This is related to recent structured representations for POMDPs; here we use stochastic situation calculus rules to specify the state transition function and the reward/value function. Finally we show that with stochastic frame axioms, actions representations in probabilistic STRIPS are exponentially larger than using the representation proposed here.