Proving properties of states in the situation calculus
Artificial Intelligence
Reasoning about knowledge and probability
Journal of the ACM (JACM)
Reasoning about knowledge
Reasoning about noisy sensors and effectors in the situation calculus
Artificial Intelligence
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Semi-structured Knowledge Representation for the Automated Financial Advisor
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Collecting commonsense experiences
Proceedings of the 2nd international conference on Knowledge capture
Collecting commonsense experiences
Proceedings of the 2nd international conference on Knowledge capture
Probabilistic complex actions in GOLOG
Fundamenta Informaticae
Learning to integrate multiple knowledge sources for case-based reasoning
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
An on-line decision-theoretic Golog interpreter
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Reasoning about attitudes of complaining customers
Knowledge-Based Systems
ICCS '08 Proceedings of the 16th international conference on Conceptual Structures: Knowledge Visualization and Reasoning
Learning communicative actions of conflicting human agents
Journal of Experimental & Theoretical Artificial Intelligence
Discovering common outcomes of agents' communicative actions in various domains
Knowledge-Based Systems
Assessing plausibility of explanation and meta-explanation in inter-human conflicts
Engineering Applications of Artificial Intelligence
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We report on a novel approach to modeling a dynamic domain with limited knowledge. A domain may include participating agents where we are uncertain about motivations and decision-making principles of some of these agents. Our reasoning setting for such domains includes deductive, inductive, and abductive components. The deductive component is based on situation calculus and describes the behavior of agents with complete information. The machine learning-based inductive and abductive components involve the previous experience with the agents, whose actions are uncertain to the system. Suggested reasoning machinery is applied to the problem of processing customer complaints in the form of textual messages that contain a multiagent conflict. The task is to predict the future actions of an opponent agent to determine the required course of action to resolve a multiagent conflict. This study demonstrates that the hybrid reasoning approach outperforms both stand-alone deductive and inductive components. Suggested methodology reflects the general situation of reasoning in dynamic domains in the conditions of uncertainty, merging analytical (rule-based) and analogy-based reasoning.