Hostname: page-component-6bf8c574d5-zc66z Total loading time: 0 Render date: 2025-03-03T20:50:12.803Z Has data issue: false hasContentIssue false
  • English
  • Français

Embracing Background Knowledge in the Analysis of Actual Causality: An Answer Set Programming Approach

Published online by Cambridge University Press:  26 July 2023

MICHAEL GELFOND Open the ORCID record for MICHAEL GELFOND [Opens in a new window] ,
JORGE FANDINNO Open the ORCID record for JORGE FANDINNO [Opens in a new window]  and
EVGENII BALAI Open the ORCID record for EVGENII BALAI [Opens in a new window]
MICHAEL GELFOND
Affiliation:
Department of Computer Science, Texas Tech University, Lubbock, TX, USA (e-mail: [email protected])
JORGE FANDINNO
Affiliation:
Department of Computer Science, University of Nebraska at Omaha, Omaha, NE, USA (e-mail: [email protected])
EVGENII BALAI
Affiliation:
Department of Computer Science, Texas Tech University, Lubbock, TX, USA (e-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper presents a rich knowledge representation language aimed at formalizing causal knowledge. This language is used for accurately and directly formalizing common benchmark examples from the literature of actual causality. A definition of cause is presented and used to analyze the actual causes of changes with respect to sequences of actions representing those examples.

Keywords

answer set programmingcausalityknowledge representation
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 23 , Issue 4: 2023 International Conference on Logic Programming , July 2023 , pp. 715 - 729
Check for updates
Copyright
© The Author(s), 2023. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Balai, E., Gelfond, M. and Zhang, Y. 2019. P-log: refinement and a new coherency condition. Annals of Mathematics and Artificial Intelligence 86, 1-3, 149–192.CrossRefGoogle Scholar
Balduccini, M. 2012. Answer set solving and non-herbrand functions. In Proceedings of the 14th International Workshop on Non-Monotonic Reasoning (NMR’2012)(Jun 2012).Google Scholar
Balduccini, M. and Gelfond, M. 2003. Logic programs with consistency-restoring rules. In International Symposium on Logical Formalization of Commonsense Reasoning, AAAI 2003 Spring Symposium Series, Vol. 102.Google Scholar
Baral, C., Gelfond, M. and Rushton, J. N. 2009. Probabilistic reasoning with answer sets. Theory and Practice of Logic Programming 9, 1, 57–144.CrossRefGoogle Scholar
Batusov, V. and Soutchanski, M. 2018. Situation calculus semantics for actual causality. In Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 32.CrossRefGoogle Scholar
Beckers, S. 2021. The counterfactual ness definition of causation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 6210–6217.Google Scholar
Beckers, S. and Vennekens, J. 2016. A general framework for defining and extending actual causation using cp-logic. International Journal of Approximate Reasoning 77, 105–126.CrossRefGoogle Scholar
Beckers, S. and Vennekens, J. 2017. The transitivity and asymmetry of actual causation. Ergo: An Open Access Journal of Philosophy 4. CrossRefGoogle Scholar
Beckers, S. and Vennekens, J. 2018. A principled approach to defining actual causation. Synth. 195, 2, 835–862.CrossRefGoogle Scholar
Bochman, A. 2018. Actual causality in a logical setting. In IJCAI. ijcai.org, 1730–1736.Google Scholar
Cabalar, P. and Fandinno, J. 2016. Enablers and inhibitors in causal justifications of logic programs. CoRR abs/1602.06897. CrossRefGoogle Scholar
Cabalar, P., Fandinno, J. and Fink, M. 2014. Causal graph justifications of logic programs. Theory Pract. Log. Program. 14, 4–5, 603–618.CrossRefGoogle Scholar
Cabalar, P., Fandinno, J., and Muñiz, B. 2020. A system for explainable answer set programming. In ICLP Technical Communications. EPTCS, vol. 325. 124–136.Google Scholar
Chockler, H. and Halpern, J. Y. 2004. Responsibility and blame: A structural-model approach. Journal of Artificial Intelligence Research 22, 93–115.CrossRefGoogle Scholar
Denecker, M., Bogaerts, B., and Vennekens, J. 2019. Explaining actual causation in terms of possible causal processes. In European Conference on Logics in Artificial Intelligence. Springer, 214–230.Google Scholar
Fandinno, J. 2016. Towards deriving conclusions from cause-effect relations. Fundamenta Informaticae 147, 1, 93–131.CrossRefGoogle Scholar
Gelfond, M. and Balai, E. 2020. Causal analysis of events occurring in trajectories of dynamic domains. In ICLP Workshops. CEUR Workshop Proceedings, vol. 2678.Google Scholar
Gelfond, M. and Kahl, Y. 2014. Knowledge Representation, Reasoning, and the Design of Intelligent Agents: The Answer-Set Programming Approach. Cambridge University Press.Google Scholar
Hall, N. 2000. Causation and the price of transitivity. Journal of Philosophy 97, 4, 198–222.CrossRefGoogle Scholar
Hall, N. 2004. Two concepts of causation. In Causation and Counterfactuals, J. Collins, N. Hall, and L. A. Paul, Eds. Cambridge, MA: Press, MIT, 225–276.Google Scholar
Hall, N. 2007. Structural equations and causation. Philosophical Studies 132, 1, 109–136.CrossRefGoogle Scholar
Halpern, J. and Hitchcock, C. 2010. Actual causation and the art of modeling. 383–406.Google Scholar
Halpern, J. Y. 2014. Appropriate causal models and stability of causation. In Principles of Knowledge Representation and Reasoning: Proceedings of the Fourteenth International Conference, KR 2014, Vienna, Austria, July 20–24, 2014. AAAI Press.Google Scholar
Halpern, J. Y. 2015. A modification of the Halpern-Pearl definition of causality. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25-31, 2015, Q. Yang and M. Wooldridge, Eds. Press, AAAI, 3022–3033.Google Scholar
Halpern, J. Y. and Pearl, J. 2001. Causes and explanations: A structural-model approach: Part 1: Causes. In Proceedings of the Seventeenth Conference in Uncertainty in Artificial Intelligence, UAI 2001, University of Washington, Seattle, Washington, USA, August 2-5, 2001. Morgan Kaufmann, 194–202.Google Scholar
Hopkins, M. and Pearl, J. 2003. Clarifying the usage of structural models for commonsense causal reasoning. In Proceedings of the AAAI Spring Symposium on Logical Formalizations of Commonsense Reasoning, 83–89.Google Scholar
Hopkins, M. and Pearl, J. 2007. Causality and counterfactuals in the situation calculus. Journal of Logic and Computation 17, 5, 939–953.CrossRefGoogle Scholar
Inclezan, D. 2016. Corealmlib: An ALM library translated from the component library. Theory and Practice of Logic Programming 16, 5–6, 800–816.Google Scholar
LeBlanc, E. C., Balduccini, M., and Vennekens, J. 2019. Explaining actual causation via reasoning about actions and change. In JELIA. Lecture Notes in Computer Science, vol. 11468. Springer, 231–246.Google Scholar
McCarthy, J. 1998. Elaboration tolerance.Google Scholar
Pearl, J. 2009. Causality. Cambridge university press.CrossRefGoogle Scholar
Vennekens, J. 2011. Actual causation in CP-logic. TPLP 11, 4–5, 647–662.Google Scholar