2014
DOI: 10.1007/978-3-319-11973-1_41
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Abstract: This paper describes an architecture that combines the complementary strengths of declarative programming and probabilistic graphical models to enable robots to represent, reason with, and learn from, qualitative and quantitative descriptions of uncertainty and knowledge. An action language is used for the low-level (LL) and high-level (HL) system descriptions in the architecture, and the definition of recorded histories in the HL is expanded to allow prioritized defaults. For any given goal, tentative plans c… Show more

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Cited by 21 publications
(30 citation statements)
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“…For instance, we are exploring the introduction of the probabilistic description of knowledge and uncertainty in conjunction with the logical reasoning. We hope to build on recent work that has been reported in this direction in the general context of using ASP and probabilistic planning with robots [33,38]. Furthermore, we are exploring the extension of KRASP and UMBRA to explicitly model communication between agents and to use this model for generating explanations.…”
Section: Discussionmentioning
confidence: 99%
See 3 more Smart Citations
“…For instance, we are exploring the introduction of the probabilistic description of knowledge and uncertainty in conjunction with the logical reasoning. We hope to build on recent work that has been reported in this direction in the general context of using ASP and probabilistic planning with robots [33,38]. Furthermore, we are exploring the extension of KRASP and UMBRA to explicitly model communication between agents and to use this model for generating explanations.…”
Section: Discussionmentioning
confidence: 99%
“…One example is a three-layered organization of knowledge and reasoning for planning and diagnostics based on first-order logic and probabilistic reasoning in open worlds [31]. Another example is a general refinement-based architecture for robots that represents and reasons with incomplete domain knowledge and uncertainty at different levels of granularity, combining non-monotonic logical reasoning and probabilistic reasoning for planning and diagnostics [32,33]. Researchers have also combined non-monotonic logical reasoning with relational reinforcement learning to discover domain axioms in response to failures that cannot be explained with existing knowledge [34].…”
Section: Related Workmentioning
confidence: 99%
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“…ASP also provides planning and diagnosis capabilities [1] not used in this paper but included in other work [27]. Although ASP has been used in the development of agent architectures, ASP does not support probabilistic modeling of uncertainty, and architectures that combine ASP with probabilistic reasoning lack key representation and reasoning capabilities (see Section II).…”
Section: A Knowledge Representation With Answer Set Prologmentioning
confidence: 99%