Predicting Agent Spatial Information: A Comparison Between Neural Networks and Dead Reckoning Algorithms

Predicting Agent Spatial Information: A Comparison Between Neural Networks and Dead Reckoning Algorithms Amy E. Henninger (amy@soartech.com) Soar Technology, Inc. 3361 Rouse Road Suite 240 Orlando, FL 32826 Avelino J. Gonzalez (ajg@isl.engr.ucf.edu) School of Electrical Engineering and Comupter Science University of Central Florida, Orlando, FL 32816 Douglas A. Reece (reeced@saic.com) SAIC 12479 Research Parkway Orlando, Florida 32817 Abstract In tune with the 24th Annual Cognitive Science Conference’s emphasis on application, this paper presents an empirical comparison between two methods used in agent tracking. The need to predict an agent’s intents or future actions has been well documented in multi-agent system’s literature and has been motivated by both systematically-practical and psychologically-principled concerns. However, little effort has focused on the comparison of predictive modeling techniques. This paper compares the performance of two predictive models both developed for the same, well-defined modeling task. Specifically, this paper compares the performance of a neural network based model and dead-reckoning model, both used to predict an agent’s trajectory and position. After introducing the background and motivation for the research, this paper reviews the form of the dead-reckoning algorithms, the architecture and training algorithms of the neural networks, the integration of the models into a large- scale simulation environment, and the means by which the performance measures are generated. Quantitative measures from our experiments indicate that, for the task considered, the neural network based model provides greater predictive utility, but at an increased cost in processing time. Performance measures are presented over increasing levels of error tolerance. Introduction Intelligent agents typically operate in an environment populated by other intelligent agents. Agents may help each other, hinder each other, get in each other’s way, or ignore each other, often without directly communicating their intent. In order for an agent to achieve its goals, it is thus sometimes necessary for the agent to determine where the other agents are, what they are doing, and what their plans are. For example, an agent may want to infer what plan an opponent is executing so that the agent can select countermoves. Han and Veloso (1995), Rao (1994), Rao and Georgeff (1995), Tambe and Rosenbloom (1995), and Tambe (1996) have studied various forms of recognizing an agent’s intents. Sometimes it is necessary to infer facts that are normally observable, such as agent location, because of sensor or other limitations. For example, a pilot agent may need to predict where a threat aircraft is flying after it enters a cloud. There are many approaches to predicting agent trajectories, including Newtonian mechanics (Lin and Ng, 1993), neural networks (Kim et al, 1999), Hidden Markov Models (Washington, 1998) and others. This paper addresses a particular application of trajectory prediction in distributed simulation and compares the effectiveness of a neural network to a commonly used Newtonian approach for this application. The remainder of this section defines the trajectory estimation problem in the distributed simulation application and describes a previous use of neural networks for estimating agent trajectory in a visual scanning application. The paper then describes a neural network approach for trajectory estimation in distributed simulation and presents results and comparisons with Newtonian dead reckoning. Dead Reckoning in Distributed Simulation In a Distributed Interactive Simulation (DIS) (DIS Steering Committee, 1994), simulation software for each agent runs independently of other agents and broadcasts the ground truth about the state of the agent through network packets known as protocol data units (PDUs). Each simulation in DIS uses trajectory estimation so that the state of the agents does not have to be broadcast frequently. Lin and Ng