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2006

Decision Making in Uncertain Real-World Domains Using DT-Golog

8 years 9 months ago
Decision Making in Uncertain Real-World Domains Using DT-Golog
DTGolog, a decision-theoretic agent programming language based on the situation calculus, was proposed to ease some of the computational difficulties associated with Markov Decision Processes (MDPs) by using natural ordering constraints on execution of actions. Using DTGolog, domain specific constraints on a set of policies can be expressed in a high-level program to reduce significantly computations required to find a policy optimal in this set. We explore whether the DTGolog framework can be used to evaluate different designs of a decision making agent in a large real-world domain. Each design is understood as combination of a template (expressed as a Golog program) for available policies and a reward function. To evaluate and compare alternative designs we estimate the probability of goal satisfaction for each design. As a domain, we choose the London Ambulance Service (LAS) case study that is well known in software engineering, but remains unknown in AI. We demonstrate that DTGolo...
Mikhail Soutchanski, Huy Pham, John Mylopoulos
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2006
Where AAAI
Authors Mikhail Soutchanski, Huy Pham, John Mylopoulos
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