Logical foundations of artificial intelligence
Logical foundations of artificial intelligence
Approximate reasoning in intelligent systems, decision and control
Approximate reasoning in intelligent systems, decision and control
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Nonmonotonic reasoning: logical foundations of common sense
Nonmonotonic reasoning: logical foundations of common sense
NPSNET: constructing a 3D virtual world
I3D '92 Proceedings of the 1992 symposium on Interactive 3D graphics
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
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Autonomous agents in computer simulations do not have the usual mechanisms to acquire information as do their human counterparts. In many such simulations, it is not desirable that the agent have access to complete and correct information about its environment. We examine how imperfection in available information may be simulated in the case of autonomous agents. We determine probabilistically what the agent may detect, through hypothetical sensors, in a given situation. These detections are combined with the agent's knowledge base to infer observations and beliefs. Inherent in this task is a degree of uncertainty in choosing the most appropriate observation or belief. We describe and compare two approaches - a numerical approach and one based on defeasible logic - for simulating an appropriate belief in light of conflicting detection values at a given point in time. We discuss the application of this technique to autonomous forces in combat simulation systems.