Artificial Intelligence
Nonmonotonic logic and temporal projection
Artificial Intelligence
Readings in nonmonotonic reasoning
The anomalous extension problem in default reasoning
Artificial Intelligence
Chronological ignorance: experiments in nonmonotonic temporal reasoning
Artificial Intelligence
Autoepistemic logic and formalization of commonsense reasoning: preliminary report
Proceedings of the 2nd international workshop on Non-monotonic reasoning
Intention is choice with commitment
Artificial Intelligence
Reasoning situated in time I: basic concepts
Journal of Experimental & Theoretical Artificial Intelligence
A simple solution to the Yale shooting problem
Proceedings of the first international conference on Principles of knowledge representation and reasoning
Fully Deadline-Coupled Planning: One Step at a Time
ISMIS '91 Proceedings of the 6th International Symposium on Methodologies for Intelligent Systems
Step-logic: reasoning situated in time
Step-logic: reasoning situated in time
Formal theories of action (preliminary report)
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
Simple causal minimizations for temporal persistence and projection
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 1
Exploiting focal points among alternative solutions: Two approaches
Annals of Mathematics and Artificial Intelligence
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Formal real-time imagination is a term that may curiously describe the activities of a commonsense agent in a real-time setting in general, and in a tight deadline situation in particular. We briefly describe an ‘active-logic’ mechanism that fits this description. Temporal projection is an essential component of realtime planning. We draw a parallel between imagination as we understand it in human context and the capacity of the automated agent to formulate mental images of possible scenarios and plans of action in the course of its reasoning. We outline a treatment of temporal issues of significance to a time-situated reasoning mechanism in a dynamic setting with deadlines. The Yale shooting problem is a benchmark problem in temporal reasoning. We demonstrate how the active-logic planning mechanism successfully handles some interesting real-time variants of the Yale shooting problem. The solutions to each of these illustrate the agent’s ability to form contexts within which to reason, to project in each context thus formed by applying default inferences, and to revise and extend its conclusions within each context by applying time-sensitive inference rules, and most importantly, to account for all the time spent in the process.