Abstract
Weighted abduction computes hypotheses that explain input observations. It employs parameters, called weights, to output hypotheses suitable for each application. This versatility makes it applicable to plant operation, cybersecurity or discourse analysis. However, the hypotheses selected by an abductive reasoner from among possible hypotheses may be inconsistent with the user’s knowledge such as an operator’s or analyst’s expertise. In order to resolve this inconsistency and generate hypotheses in accordance with the user’s knowledge, this paper proposes two user-feedback dialogue protocols in which the user points out, either positively or negatively, properties of the hypotheses presented by the reasoner, and the reasoner regenerates hypotheses that satisfy the user’s feedback. As a minimum requirement for user-feedback dialogue protocols, we then prove that our protocols necessarily terminate under certain reasonable conditions and achieve a fixed target hypothesis if the user determines the positivity or negativity of each pointed-out property based on whether the target hypothesis has that property.
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References
Aliseda, A.: The logic of abduction: an introduction. In: Magnani, L., Bertolotti, T. (eds.) Springer Handbook of Model-Based Science. SH, pp. 219–230. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-30526-4_10
Appelt, D.E., Pollack, M.E.: Weighted abduction for plan ascription. Artif. Intell. 2(1), 1–25 (1992). https://doi.org/10.1007/BF01101857
Black, E., Hunter, A.: An inquiry dialogue system. Auton. Agent. Multi-Agent Syst. 19(2), 173–209 (2009). https://doi.org/10.1007/s10458-008-9074-5
Hobbs, J.R., Stickel, M., Martin, P., Edwards, D.: Interpretation as abduction. In: 26th Annual Meeting of the Association for Computational Linguistics, Buffalo, New York, USA, pp. 95–103. Association for Computational Linguistics (1988). https://doi.org/10.3115/982023.982035. https://aclanthology.org/P88-1012
Hutchins, E.M., Cloppert, M.J., Amin, R.M.: Intelligence-driven computer network defense informed by analysis of adversary campaigns and intrusion kill chains. In: Proceedings of the 6th International Conference on Information Warfare and Security (ICIW 2011), pp. 113–125. Academic Conferences Ltd. (2011). http://www.lockheedmartin.com/content/dam/lockheed/data/corporate/documents/LMWhite-Paper-Intel-Driven-Defense.pdf
Inoue, N., Ovchinnikova, E., Inui, K., Hobbs, J.: Weighted abduction for discourse processing based on integer linear programming, Chap. 2. In: Sukthankar, G., Geib, C., Bui, H.H., Pynadath, D.V., Goldman, R.P. (eds.) Plan, Activity, and Intent Recognition, pp. 33–55. Morgan Kaufmann, Boston (2014). https://doi.org/10.1016/B978-0-12-398532-3.00002-6
Motoura, S., Hoshino, A., Hosomi, I.: Cooperative hypothesis building for computer security incident response. In: Senjyu, T., Mahalle, P.N., Perumal, T., Joshi, A. (eds.) ICT with Intelligent Applications. SIST, vol. 248, pp. 489–497. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-4177-0_49
Motoura, S., Yamamoto, K., Kubosawa, S., Onishi, T.: Translating MFM into FOL: towards plant operation planning. In: Gofuku, A. (ed.) Proceedings of the Third International Workshop on Functional Modelling for Design and Operation of Engineering Systems (2018)
Paul, G.: Approaches to abductive reasoning: an overview. J. Log. Comput. 7(2), 109–152 (1993). https://doi.org/10.1007/BF00849080
Stickel, M.E.: A prolog-like inference system for computing minimum-cost abductive explanations in natural-language interpretation. Ann. Math. Artif. Intell. 4(1), 89–105 (1991). https://doi.org/10.1007/BF01531174
The MITRE Corporation: MITRE ATT &CK (2022). https://attack.mitre.org/. Accessed 4 Jan 2022
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Motoura, S., Hoshino, A., Hosomi, I., Sadamasa, K. (2024). A Logical Framework for User-Feedback Dialogues on Hypotheses in Weighted Abduction. In: Bouraoui, Z., Vesic, S. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2023. Lecture Notes in Computer Science(), vol 14294. Springer, Cham. https://doi.org/10.1007/978-3-031-45608-4_4
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DOI: https://doi.org/10.1007/978-3-031-45608-4_4
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