BYTE - Lecture notes in computer science Vol. 174
Artificial Intelligence
Artificial Intelligence
A logic-based calculus of events
New Generation Computing
Causes for events: their computation and applications
Proc. of the 8th international conference on Automated deduction
A logical framework for default reasoning
Artificial Intelligence
Proofs and types
Proceedings of the Seventh Conference (AISB89) on Artificial Intelligence and Simulation of Behaviour
On constrained default theories
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
An assumption-based framework for non-monotonic reasoning
Proceedings of the second international workshop on Logic programming and non-monotonic reasoning
Handbook of logic in artificial intelligence and logic programming (vol. 3)
The situation calculus and event calculus compared
ILPS '94 Proceedings of the 1994 International Symposium on Logic programming
A comparative study of open default theories
Artificial Intelligence
Time and norms: a formalisation in the event-calculus
ICAIL '99 Proceedings of the 7th international conference on Artificial intelligence and law
ACM Computing Surveys (CSUR)
Animated specifications of computational societies
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
Assumption-Based Modeling Using ABEL
ECSQARU/FAPR '97 Proceedings of the First International Joint Conference on Qualitative and Quantitative Practical Reasoning
Two-Sided Hypotheses Generation for Abductive Analogical Reasoning
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Computing scenario from knowledge with preferentially ordered hypotheses
Systems and Computers in Japan
Assumption based multi-valued semantics for extended logic programs
ISMVL '06 Proceedings of the 36th International Symposium on Multiple-Valued Logic
Defeasible Reasoning with e-Contracts
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
The role of assumption identification in autonomous agent reasoning
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Multi-agent assumption-based planning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
The representation of e-contracts as default theories
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Found ations of assumption-based truth maintenance systems: preliminary report
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 1
WISE'05 Proceedings of the 6th international conference on Web Information Systems Engineering
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In this paper we address dynamic assumption-based reasoning in open agent systems, where, unavoidably, agents have incomplete knowledge about their environment and about other agents. The interactions among agents in such systems are typically subject to norms, which stipulate what each agent is obliged, permitted, prohibited, empowered etc. to do, while it participates in the system. In such environments agents need to resort to assumptions, in order to establish what actions are appropriate to perform, and they need to do so dynamically, since the environment, the agents that exist in it, the information that is exchanged between them, and the normative relations between them change over time. In earlier work, we had proposed Default Theory construction to support dynamic assumption-based reasoning. We argued that in this way, agents could perform both assumption identification and employment dynamically, contrary to other approaches to assumption-based reasoning, which catered for either one or the other. A shortcoming of this early proposal of ours, though, is that Default Theory construction seems to require proof, which is notably computationally expensive. In this paper we present a computational technique that can be used for this construction in an incremental manner that does not depend on proof, and a prototype tool that we developed for experimentation. In a nutshell, depending on their current knowledge at any given time, agents can identify appropriate candidate assumptions in an ad hoc manner. When such choices need to be revised, agents can reconstruct their view of the possible world in which they find themselves, and establish their revised assumption requirements at run-time. This paper is an extended version of the work presented at the 2008 IEEE/WI/ACM Int. Conf. on Intelligent Agent Technology, 9-12 December, Sydney, Australia.