AAAI Publications, The Thirtieth International Flairs Conference

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Modeling Temporally Dynamic Environments for Persistent Autonomous Agents
Matthew Joseph O'Brien, Ronald Craig Arkin

Last modified: 2017-05-08


This paper explores how an autonomous agent can model dynamic environments and use that knowledge to improve its behavior. This capability is of particular importance for persistent agents, or long-term autonomy. Inspiration is drawn from circadian rhythms in nature, which drive periodic behavior in many organisms. In our approach, the chemical oscillators from nature are replaced with methods from time series analysis designed for forecasting complex season patterns. This model is incorporated into a behavior-based architecture as an advanced-percept, providing future estimates of the environment rather than current measurements. A simulated application of a janitor robot working in an environment with heavy pedestrian traffic was created as a testbed. Experimental data used real world pedestrian traffic counts and showed an agent using online forecasting of future traffic outperformed both a reactive, sensor-based, strategy and a strategy with a deterministic schedule.


long-term autonomy; time series; behavior-based robotics; circadian rhythms

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