Knowledge Representation, Reasoning, and the Design of Intelligent Agents: The Answer-Set Programming Approach
Abstract
Knowledge representation and reasoning is the foundation of artificial intelligence, declarative programming, and the design of knowledge-intensive software systems capable of performing intelligent tasks. Using logical and probabilistic formalisms based on answer set programming (ASP) and action languages, this book shows how knowledge-intensive systems can be given knowledge about the world and how it can be used to solve non-trivial computational problems. The authors maintain a balance between mathematical analysis and practical design of intelligent agents. All the concepts, such as answering queries, planning, diagnostics, and probabilistic reasoning, are illustrated by programs of ASP. The text can be used for AI-related undergraduate and graduate classes and by researchers who would like to learn more about ASP and knowledge representation.
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s(CASP) is well-suited to the representation of complex world states and provides an advantage over other ASP systems.
“This advantage makes s(CASP) well-suited to the representation of complex world states and provides an advantage over other ASP systems [5].”
From Autonomous Task Completion Based on Goal-directed Answer Set Programming (2025) · cites this paper as [5]
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Mathew, D. E., Ebem, D. U., Ikegwu, A. C., Ukeoma, P. E., & Dibiaezue, N. F. (2025) · Neural Processing Letters · doi:10.1007/s11063-025-11732-2 · Cited by 168 · see citing works
SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning
Rana, K., Haviland, J., Garg, S., Abou-Chakra, J., Reid, I., & Suenderhauf, N. (2023) · arXiv · doi:10.48550/arxiv.2307.06135 · Cited by 29 · see citing works
“Conventional methods employ domain-specific languages such as PDDL [20, 21, 22] and ASP [23] together with semantic parsing [24, 25], search techniques [26, 27] and complex heuristics [28] to arrive at a solution.”
Reliable Natural Language Understanding with Large Language Models and Answer Set Programming
Rajasekharan, A., Zeng, Y., Padalkar, P., & Gupta, G. (2023) · Electronic Proceedings in Theoretical Computer Science · doi:10.4204/eptcs.385.27 · Cited by 26 · see citing works
Fu, T., Jauw, B., & Sridharan, M. (2025) · 2025 34th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) · doi:10.1109/ro-man63969.2025.11217618 · Cited by 3 · see citing works
Autonomous Task Completion Based on Goal-directed Answer Set Programming
Tudor, A. R. (2025) · Electronic Proceedings in Theoretical Computer Science · doi:10.4204/eptcs.416.39
Li, F., Zuo, F., & Gupta, G. (2025) · arXiv · doi:10.48550/arxiv.2512.04908 · Cited by 1 · see citing works
- Authors
- Gelfond, M., & Kahl, Y.
- Affiliations
- Texas Tech University (US)
- ORCID
- Gelfond, Michael
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2014-03-10
- OpenAlex
- W3155306957
- Cited by
- 253
- References
- 0
- Citations by year
- 2026: 11 · 2025: 25 · 2024: 7 · 2023: 28 · 2022: 18 · 2021: 20 · 2020: 19 · 2019: 35 · 2018: 24 · 2017: 20 · 2016: 16 · 2015: 13 · 2014: 12 · 2013: 4
- FWCI
- 8.89
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Cambridge University Press eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
- Keywords
- knowledge representation, automated reasoning, intelligent agents, answer set programming, action languages, planning, diagnostics, probabilistic reasoning, query answering, declarative programming
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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