Inferring the intentional states of autonomous virtual agents
Inferring the intentional states of autonomous virtual agents
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DOI：
10.1016/j.cognition.2013.11.011
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发表时间：
2014-03
期刊：
影响因子：
3.4
通讯作者：
Peter C. Pantelis;Chris L. Baker;Steven A. Cholewiak;Kevin Sanik;A. Weinstein;Chia-Chien Wu;J. Tenenbaum;J. Feldman
中科院分区：
文献类型：
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作者：
Peter C. Pantelis;Chris L. Baker;Steven A. Cholewiak;Kevin Sanik;A. Weinstein;Chia-Chien Wu;J. Tenenbaum;J. Feldman
Inferring the mental states of other agents, including their goals and intentions, is a central problem in cognition. A critical aspect of this problem is that one cannot observe mental states directly, but must infer them from observable actions. To study the computational mechanisms underlying this inference, we created a two-dimensional virtual environment populated by autonomous agents with independent cognitive architectures. These agents navigate the environment, collecting “food” and interacting with one another. The agents’ behavior is modulated by a small number of distinct goal states:attacking,exploring,fleeing, andgathering food. We studied subjects’ ability to detect and classify the agents’ continually changing goal states on the basis of their motions and interactions. Although the programmed ground truth goal state is not directly observable, subjects’ responses showed both high validity (correlation with this ground truth) and high reliability (correlation with one another). We present a Bayesian model of the inference of goal states, and find that it accounts for subjects’ responses better than alternative models. Although the model is fit to the actual programmed states of the agents, and not to subjects’ responses, its output actually conforms better to subjects’ responses than to the ground truth goal state of the agents.
