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This paper presents a simple and efficient method of modeling synthetic vision, memory, and learning for autonomous animated characters in real-time virtual environments. The model is efficient in terms of both storage requirements and update times, and can be flexibly combined with a variety of higher-level reasoning modules or complex memory rules. The design is inspired by research in motion planning, control, and sensing for autonomous mobile robots. We apply this framework to the problemdoi:10.1109/ca.1999.781205 dblp:conf/ca/KuffnerL99 fatcat:ti5lbhljxjgrxctbkhs22s4azm