Soft systems methodology in action
Soft systems methodology in action
The independent choice logic for modelling multiple agents under uncertainty
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Computational intelligence: a logical approach
Computational intelligence: a logical approach
Symbolic knowledge extraction from trained neural networks: a sound approach
Artificial Intelligence
Logic for Problem Solving
From logic programming towards multi-agent systems
Annals of Mathematics and Artificial Intelligence
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Verifiable agent interaction in abductive logic programming: The SCIFF framework
ACM Transactions on Computational Logic (TOCL)
Integrating Logic Programming and Production Systems in Abductive Logic Programming Agents
RR '09 Proceedings of the 3rd International Conference on Web Reasoning and Rule Systems
Evolution prospection in decision making
Intelligent Decision Technologies
EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
Modelling morality with prospective logic
EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
The halt condition in genetic programming
EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
Computational logic in an object-oriented world
Reasoning, Action and Interaction in AI Theories and Systems
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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.