Abstract
Contemporary systems situated in real-world open environments frequently have to cope with incomplete and inconsistent information that typically increases complexity of reasoning and decision processes. Realistic modeling of such informationally complex environments calls for nuanced tools. In particular, incomplete and inconsistent information should neither trivialize nor stop both reasoning or planning. The paper introduces ACTLOG, a rule-based four-valued language designed to specify actions in a paraconsistent and paracomplete manner. ACTLOG is an extension of 4QLBel, a language for reasoning with paraconsistent belief bases. Each belief base stores multiple world representations. In this context, ACTLOG’s action may be seen as a belief bases’ transformer. In contrast to other approaches, ACTLOG actions can be executed even when the underlying belief base contents is inconsistent and/or partial. ACTLOG provides a nuanced action specification tools, allowing for subtle interplay among various forms of nonmonotonic, paraconsistent, paracomplete and doxastic reasoning methods applicable in informationally complex environments. Despite its rich modeling possibilities, it remains tractable. ACTLOG permits for composite actions by using sequential and parallel compositions as well as conditional specifications. The framework is illustrated on a decontamination case study known from the literature.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Abiteboul, S., Hull, R., Vianu, V.: Foundations of databases. Addison-Wesley Pub. Co., Reading (1996)
Belnap, N.: How a Computer Should Think. In: Ryle, G. (ed.) Contemporary Aspects of Philosophy, pp 30–55. Oriel Press, Stocksfield (1977)
Bertossi, L., Hunter, A., Schaub, T.: Introduction to inconsistency tolerance. In: Bertossi et al. (ed.). Inconsistency Tolerance, LNCS, pp. 1–14
Bertossi, L., Hunter, A., Schaub, T. (eds.): Inconsistency Tolerance, LNCS, vol. 3300. Springer, Berlin (2005)
Białek, Ł., Dunin-Kęplicz, B., Szałas, A.: Rule-Based Reasoning with Belief Structures. In: Kryszkiewicz, M., Appice, A., Ślęzak, D., Rybiński, H., Skowron, A., RaŚ, Z. (eds.) Foundations of Intelligent Systems, Proceedings of ISMIS Conference. LNAI, vol. 10352, pp 229–239. Springer (2017)
Białek, Ł., Dunin-Kęplicz, B., Szałas, A.: Towards a Paraconsistent Approach to Actions in Distributed Information-Rich Environments. In: Ivanović, M., Bădică, C., Dix, J., Jovanović, Z., Malgeri, M., Savić, M. (eds.) Proceedings of IDC - Intelligent Distributed Computing XI. Studies in Computational Intelligence, vol. 737, pp 49–60. Springer (2017)
Doherty, P., Kvarnström, J.: TALplanner: A temporal logic based forward chaining planner. Ann. Math. Artif. Intell. 30, 119–169 (2001)
Doherty, P., Kvarnström, J.: TALplanner: A temporal logic-based planner. AI Mag. 22(3), 95–102 (2001)
Doherty, P., Kvarnström, J.: The Handbook of Knowledge Representation. In: Lifschitz, V., Van Harmelen, F., Porter, F. (eds.) , pp 709–757. Elsevier (2008)
Doherty, P., Kvarnström, J., Szałas, A.: Temporal Composite Actions with Constraints. In: Brewka, G., Eiter, T., Mcilraith, S. (eds.) Proceedings of 13Th International Conference KR: Principles of Knowledge Representation and Reasoning, pp 478–488. AAAI Press (2012)
Doherty, P., Szałas, A.: Stability, supportedness, minimality and Kleene Answer Set Programs. In: Eiter, T., Strass, H., Truszczyński, M., Woltran, S. (eds.) Advances in Knowledge Representation, Logic Programming, and Abstract Argumentation, LNCS, vol. 9060, pp 125–140. Springer International Publishing (2015)
Dunin-Kęplicz, B., Szałas, A.: Epistemic Profiles and Belief Structures. In: Proceedings of KES-AMSTA 2012: Agents and Multi-Agent Systems: Technologies and Applications. LNCS, vol. 7327, pp 360–369. Springer (2012)
Dunin-Kęplicz, B., Szałas, A.: Taming complex beliefs. Trans. Comput. Collective Intell. XI LNCS 8065, 1–21 (2013)
Dunin-Kęplicz, B., Szałas, A.: Indeterministic Belief Structures. In: Jezic, G., Kusek, M., Lovrek, I., Howlett, J., Lakhmi, J. (eds.) Agent and Multi-Agent Systems: Technologies and Applications: Proceedings of 8th International Conference KES-AMSTA, pp 57–66. Springer (2014)
Dunin-Kęplicz, B., Verbrugge, R.: Teamwork in Multi-Agent systems. a formal approach. Wiley, New York (2010)
Dunin-Kęplicz, B., Verbrugge, R., Ślizak, M.: TeamLog in action: a case study in teamwork. Comput. Sci. Inf. Syst. 7(3), 569–595 (2010)
Edelkamp, S., Hoffmann, J.: PDDL2: The language for the classical part of the 4th international planning competition. In: Proceedings of the 4th International Planning Competition (2004)
Eiter, T., Faber, W., Leone, N., Pfeifer, G., Polleres, A.: Planning under Incomplete Knowledge. In: Lloyd, J., Dahl, V., Furbach, U., Kerber, M., Lau, K.K., Palamidessi, C., Pereira, L., Sagiv, Y., Stuckey, P. (eds.) Proceedings of Computational Logic: 1St International Conference, pp 807–821. Springer (2000)
Eiter, T., Faber, W., Pfeifer, G.: Declarative Planning and Knowledge Representation in an Action Language. In: Sugumaran, V. (ed.) Intelligent Information Technologies: Concepts, Methodologies, Tools, and Applications, pp 192–221. IGI Global (2008)
Fagin, R., Halpern, J., Moses, Y., Vardi, M.: Reasoning about knowledge the. MIT Press, Cambridge (2003)
Ferraris, P., Lifschitz, V.: On the Minimality of Stable Models. In: Balduccini, M., Son, T. (eds.) Logic Programming, Knowledge Representation, and Nonmonotonic Reasoning. LNCS, vol. 6565, pp 64–73. Springer (2011)
Fikes, R.E., Nilsson, N.J.: STRIPS: a new approach to the application of theorem proving to problem solving. In: Proceedings of the 2Nd International Joint Conference on Artificial Intelligence, pp. 608–620. IJCAI’71, Morgan Kaufmann Publishers Inc. (1971)
Fitting, M.: Bilattices are Nice Things. In: Proceedings of Philog Conference on Self-Reference. The Danish Network for Philosophical Logic and Its Applications, Copenhagen (2002)
Fitting, M.C.: Bilattices in Logic Programming. In: Epstein, G. (ed.) 20Th International Symposium on Multiple-Valued Logic, pp 238–247. IEEE CS Press, Los Alamitos (1990)
Ginsberg, M.: Multi-Valued Logics. In: 5Th National Conference on AI Proceedings of AAAI-86. pp. 243–247 (1986)
Ginsberg, M.: Multivalued logics: a uniform approach to reasoning in AI. Comput. Intell. 4, 256–316 (1988)
Giunchiglia, E., Lee, J., Lifschitz, V., Mc-Cain, N., Turner, H.: Nonmonotonic causal theories. Artif. Intell. 153(1-2), 49–104 (2004)
Hewitt, C.: Formalizing common sense for scalable inconsistency-robust information integration using Direct Logic reasoning and the actor model. arXiv:0812.4852 (2008)
Hewitt, C., Woods, J. (eds.): Inconsistency Robustness. College Publications (2015)
Kowalski, R., Sergot, M.: A logic-based calculus of events. N. Gener. Comput. 4(1), 67–95 (1986)
Lever, J., Richards, B.: parcPlan: a Planning Architecture with Parallel Actions, Resources and Constraints. In: Raṡ, Z. W., Zemankova, M. (eds.) Methodologies for Intelligent Systems, pp 213–222. Springer Berlin Heidelberg, Berlin (1994)
Levesque, H., Pirri, F., Reiter, R.: Foundations for the situation calculus. Electron. Trans. AI 2(3-4), 159–178 (1998)
Levesque, H., Reiter, R., Lespérance, Y., Lin, F., Scherl, R.: GOLOG: a logic programming language for dynamic domains. J. Log. Program. 31, 59–84 (1997)
Małuszyński, J., Szałas, A.: Living with Inconsistency and Taming Nonmonotonicity. In: De Moor, O., Gottlob, G., Furche, T., Sellers, A. (eds.) Datalog Reloaded. LNCS, vol. 6702, pp 384–398. Springer (2011)
Małuszyński, J., Szałas, A.: Logical foundations and complexity of 4QL, a query language with unrestricted negation. J. Appl. Non-Class. Log. 21(2), 211–232 (2011)
Małuszyński, J., Szałas, A.: Partiality and Inconsistency in Agents’ Belief Bases. In: Barbucha, D., Le, M., Howlett, R., Jain, L. (eds.) KES-AMSTA. Frontiers in Artificial Intelligence and Applications, vol. 252, pp 3–17. IOS Press (2013)
Małuszyński, J., Szałas, A., Vitória, A.: Paraconsistent Logic Programs with Four-Valued Rough Sets. In: Chan, C.C., Grzymala-Busse, J., Ziarko, W. (eds.) Proceedings of 6Th International Conference on Rough Sets and Current Trends in Computing (RSCTC 2008). LNAI, vol. 5306, pp 41–51 (2008)
McCarthy, J., Laboratory, S.A.I.: Situations, Actions, and Causal Laws. Memo (Stanford Artificial Intelligence Project), Stanford University, AI Project (1963)
Mcilraith, S., Fadel, R.: Planning with Complex Actions. In: Proceedings NMR’02, pp. 356–364 (2002)
Mueller, E.: Commonsense reasoning. An Event Calculus Based Approach. Morgan Faufmann, San Mateo (2006)
Regnier, P., Fade, B.: Complete Determination of Parallel Actions and Temporal Optimization in Linear Plans of Action. In: European Workshop on Planning, pp 100–111. Springer, Berlin (1991)
Reiter, R.: The Frame Problem in the Situation Calculus: a Simple Solution (Sometimes) and a Completeness Result for Goal Regression. In: Lifshitz, V. (ed.) Artificial Intelligence and Mathematical Theory of Computation: Papers in Honour of John Mccarthy, pp 359–380. Academic Press Professional Inc. (1991)
Reiter, R.: Knowledge in action: Logical foundations for specifying and implementing dynamical systems. MIT Press, Cambridge (2001)
Sakama, C., Inoue, K.: An alternative approach to the semantics of disjunctive logic programs and deductive databases. J. Autom. Reason. 13(1), 145–172 (1994)
Sandewall, E.: Features and Fluents: The Representation of Knowledge about Dynamical Systems, vol. 1 Clarendon Press (1994)
Shepherdson, J.: Negation in Logic Programming. In: Minker, J. (ed.) Foundations of Deductive Databases and Logic Programming, pp 19–88, Morgan Kaufmann (1988)
Shieber, S.M.: Solving Problems in an Uncertain World. Bachelor’s thesis, Harvard College (1981)
Shoham, Y.: Reasoning about change: Time and causation from the standpoint of artificial intelligence. MIT Press, Cambridge (1987)
Soininen, T., Niemelȧ, I.: Developing a Declarative Rule Language for Applications in Product Configuration. In: Gupta, G. (ed.) Proceedings of PADL’99. LNCS, vol. 1551, pp 305–319. Springer (1999)
Szałas, A.: How an agent might think. Log. J. IGPL 21(3), 515–535 (2013)
Thielscher, M.: Introduction to the fluent calculus. Electron. Trans. AI 2(3-4), 179–192 (1998)
Thielscher, M.: FLUX: a logic programming method for reasoning agents. Theory Pract. Log. Programm. 5(4-5), 533–565 (2005)
Thielscher, M.: Reasoning robots: The art and science of programming robotic agents. Springer, Berlin (2011)
Thrun, S., Burgard, W., Fox, D.: Probabilistic robotics (intelligent robotics and autonomous agents). The MIT Press, Cambridge (2005)
Vitória, A., Małuszyński, J., Szałas, A.: Modeling and reasoning with paraconsistent rough sets. Fund. Inf. 97(4), 405–438 (2009)
Wilkins, D.E.: Domain-independent planning representation and plan generation. Artif. Intell. 22(3), 269–301 (1984)
Wilkins, D.E., Myers, K.L., Lowrance, J.D., Wesley, L.P.: Planning and reacting in uncertain and dynamic environments. J. Exper. Theor. Artif. Intell. 7(1), 121–152 (1995)
Zadeh, L.: Fuzzy sets. Inf. Control 8, 333–353 (1965)
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supported by the Polish National Science Centre grant 2015/19/B/ST6/02589, the ELLIIT Network Organization for Information and Communication Technology, and the Swedish Foundation for Strategic Research FSR (SymbiKBot Project).
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
About this article
Cite this article
Białek, Ł., Dunin-Kęplicz, B. & Szałas, A. A paraconsistent approach to actions in informationally complex environments. Ann Math Artif Intell 86, 231–255 (2019). https://doi.org/10.1007/s10472-019-09627-9
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10472-019-09627-9