154 research outputs found
An Answer Set Programming-based Implementation of Epistemic Probabilistic Event Calculus
We describe a general procedure for translating Epistemic Probabilistic Event Calculus (EPEC) action language domains into Answer Set Programs (ASP), and show how the Python-driven features of the ASP solver Clingo can be used to provide efficient computation in this probabilistic setting. EPEC supports probabilistic, epistemic reasoning in domains containing narratives that include both an agent’s own action executions and environmentally triggered events. Some of the agent’s actions may be belief-conditioned, and some may be imperfect sensing actions that alter the strengths of previously held beliefs. We show that our ASP implementation can be used to provide query answers that fully correspond to EPEC’s own declarative, Bayesian-inspired semantics
On Plans With Loops and Noise
In an influential paper, Levesque proposed a formal specification for
analysing the correctness of program-like plans, such as conditional plans,
iterative plans, and knowledge-based plans. He motivated a logical
characterisation within the situation calculus that included binary sensing
actions. While the characterisation does not immediately yield a practical
algorithm, the specification serves as a general skeleton to explore the
synthesis of program-like plans for reasonable, tractable fragments.
Increasingly, classical plan structures are being applied to stochastic
environments such as robotics applications. This raises the question as to what
the specification for correctness should look like, since Levesque's account
makes the assumption that sensing is exact and actions are deterministic.
Building on a situation calculus theory for reasoning about degrees of belief
and noise, we revisit the execution semantics of generalised plans. The
specification is then used to analyse the correctness of example plans.Comment: Proceedings of AAMAS 201
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