PREGO: An Action Language for Belief-Based Cognitive Robotics in Continuous Domains

Authors

  • Vaishak Belle University of Toronto
  • Hector Levesque University of Toronto

DOI:

https://doi.org/10.1609/aaai.v28i1.8865

Keywords:

cognitive robotics, continuous uncertainty, logic and probability, reasoning about action, reasoning about knowledge

Abstract

The area of cognitive robotics is often subject to the criticism that the proposals investigated in the literature are too far removed from the kind of continuous uncertainty and noise seen in actual real-world robotics. This paper proposes a new language and an implemented system, called PREGO, based on the situation calculus, that is able to reason effectively about degrees of belief against noisy sensors and effectors in continuous domains. It embodies the representational richness of conventional logic-based action languages, such as context-sensitive successor state axioms, but is still shown to be efficient using a number of empirical evaluations. We believe that PREGO is a powerful framework for exploring real-time reactivity and an interesting bridge between logic and probability for cognitive robotics applications.

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Published

2014-06-21

How to Cite

Belle, V., & Levesque, H. (2014). PREGO: An Action Language for Belief-Based Cognitive Robotics in Continuous Domains. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1). https://doi.org/10.1609/aaai.v28i1.8865

Issue

Section

AAAI Technical Track: Knowledge Representation and Reasoning