What Happened and Why? A Mixed Architecture for Planning and Explanation Generation in Robotics
Year: 2015
Type: article
Abstract: This paper describes a mixed architecture that cou-ples the non-monotonic logical reasoning capabili-ties of a declarative language with probabilistic be-lief revision, enabling robots to represent and rea-son with qualitative and quantitative descriptions of knowledge and uncertainty. Incomplete domain knowledge, including information that holds in all but a few exceptional situations, is represented as a Answer Set Prolog (ASP) program. The answer set obtained by solving this program is used for inference, planning, and for jointly explaining (a) unexpected action outcomes; and (b) partial scene descriptions extracted from sensor input. For any given task, each action in the plan contained in the answer set is executed probabilistically. For each such action, observations extracted from sen-sor inputs perform incremental Bayesian updates to a probabilistic (belief) distribution over a relevant subset of the domain, committing high probability beliefs as statements to the ASP program. The ar-chitecture’s capabilities are evaluated in simulation and on a mobile robot in scenarios that mimic a robot waiter assisting in a restaurant. 1
Authors: Zenon Colaco, Mohan Sridharan
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7096179755