Abstract
This paper describes a mixed architecture that couples the non-monotonic logical reasoning capabilities of a declarative language with probabilistic belief revision, enabling robots to represent and reason 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 sensor 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 architecture’s capabilities are evaluated in simulation and on a mobile robot in scenarios that mimic a robot waiter assisting in a restaurant.
| Original language | English |
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| Title of host publication | Australasian Conference on Robotics and Automation 2015 (ACRA) |
| Publication status | Published - 2 Dec 2015 |