Abstract
Abductive logic programming (ALP) can be used to model reactive, proactive and pre-active thinking in intelligent agents. Reactive thinking assimilates observations of changes in the environment, whereas proactive thinking reduces goals to sub-goals and ultimately to candidate actions. Pre-active thinking generates logical consequences of candidate actions, to help in deciding between the alternatives. These different ways of thinking are compatible with any way of deciding between alternatives, including the use of both decision theory and heuristics.
The different forms of thinking can be performed as they are needed, or they can be performed in advance, transforming high-level goals and beliefs into lower-level condition-action rule form, which can be implemented in neural networks. Moreover, the higher-level and lower-level representations can operate in tandem, as they do in dual-process models of thinking. In dual process models, intuitive processes form judgements rapidly, sub-consciously and in parallel, while deliberative processes form and monitor judgements slowly, consciously and serially.
ALP used in this way can not only provide a framework for constructing artificial agents, but can also be used as a cognitive model of human agents. As a cognitive model, it combines both a descriptive model of how humans actually think with a normative model of humans can think more effectively.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Baron, J.: Thinking and Deciding, 2nd edn. Cambridge University Press, Cambridge (1994)
Checkland, P.: Soft Systems Methodology: a thirty year retrospective. John Wiley, Chichester (1999)
Kahneman, D., Shane, F.: Representativeness revisited: Attributive substitution in intuitive judgement. In: Heuristics of Intuitive Judgement: Extensions and Applications. Cambridge University Press, Cambridge (2002)
Kakas, T., Kowalski, R., Toni, F.: The Role of Logic Programming in Abduction. In: Gabbay, D., Hogger, C.J., Robinson, J.A. (eds.) Handbook of Logic in Artificial Intelligence and Programming 5, pp. 235–324. Oxford University Press, Oxford (1998)
Kowalski, R.: Logic for Problem Solving. North-Holland, Elsevier (1979)
Kowalski, R.: How to be artificially intelligent (2002-2006), http://www.doc.ic.ac.uk/~rak/
Kowalski, R., Sadri, F.: From Logic Programming towards Multi-agent Systems. Annals of Mathematics and Artificial Intelligence 25, 391–419 (1999)
Poole, D.L.: The independent choice logic for modeling multiple agents under uncertainty. Artificial Intelligence 94, 7–56 (1997)
Poole, D.L., Mackworth, A.K., Goebel, R.: Computational intelligence: a logical approach. Oxford University Press, Oxford (1998)
Smith, E.R., DeCoster, J.: Dual-Process Models in Social and Cognitive Psychology: Conceptual Integration and Links to Underlying Memory Systems. Personality and Social Psychology Review 4, 108–131 (2000)
Thagard, P.: Mind: Introduction to Cognitive Science. MIT Press, Cambridge (1996)
Vickers, G.: The Art of Judgement. Chapman and Hall, London (1965)
Frawley, W.: Control and Cross-Domain Mental Computation: Evidence from Language Breakdown. Computational Intelligence 18(1), 1–28 (2002)
d’Avila Garcez, A.S., Broda, K., Gabbay, D.M.: Symbolic knowledge extraction from trained neural networks: A sound approach. Artificial Intelligence 125, 155–207 (2001)
Holldobler, S., Kalinke, Y.: Toward a new massively parallel computational model for logic programming. In: Proceedings of the Workshop on Combining Symbolic and Connectionist Processing, ECAI 1994, pp. 68–77 (1994)
Stenning, K., van Lambalgen, M.: Semantic interpretation as computation in nonmonotonic logic. Cognitive Science (2006)
Nicolas, J.M., Gallaire, H.: Database: Theory vs. interpretation. In: Gallaire, H., Minker, J. (eds.) Logic and Databases. Plenum, New York (1978)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kowalski, R. (2006). The Logical Way to Be Artificially Intelligent. In: Toni, F., Torroni, P. (eds) Computational Logic in Multi-Agent Systems. CLIMA 2005. Lecture Notes in Computer Science(), vol 3900. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11750734_1
Download citation
DOI: https://doi.org/10.1007/11750734_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33996-0
Online ISBN: 978-3-540-33997-7
eBook Packages: Computer ScienceComputer Science (R0)