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2022

The Quest for a Common Model of the Intelligent Decision Maker

Sutton, Richard S.

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The premise of the Multi-disciplinary Conference on Reinforcement Learning and Decision Making is that multiple disciplines share an interest in goal-directed decision making over time.

The idea of this paper is to sharpen and deepen this premise by proposing a perspective on the decision maker that is substantive and widely held across psychology, artificial intelligence, economics, control theory, and neuroscience, which I call the "common model of the intelligent agent".
The common model does not include anything specific to any organism, world, or application domain.
The common model does include aspects of the decision maker's interaction with its world (there must be input and output, and a goal) and internal components of the decision maker (for perception, decision-making, internal evaluation, and a world model).

The Quest for a Common Model of the Intelligent Decision Maker · Around

Built on

The Nature of Explanation
Craik, K. J. W. (1943) · 1943
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Tolman, E. C. (1948). Cognitive maps in rats and men. Psychological review, 55 (4), 189
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Hochreiter, S., Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9 (8), 1735–1780
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Earlier work this paper cites.

Similar

A neural substrate of prediction and reward
Schultz, W., Dayan, P., Montague, P. R. (1997) · 1997
Cited alongside, same era.
Littman, M. L., Sutton, R. S., Singh, S. (2002). Predictive representations of state. In Advances in Neural Information Processing Systems 14 , pp. 1555–1561. MIT Press, Cambridge, MA
2002
Cited alongside, same era.
Glimcher, P. W. (2011). Understanding dopamine and reinforcement learning: The dopamine reward prediction error hypothesis. Proceedings of the National Academy of Sciences, 108 (Supplement 3), 15647–15654
2011
Cited alongside, same era.

Then

Kahneman, D. (2011). Thinking, Fast and Slow . Macmillan
2011
Later among the works it cites.
Schrittwieser, J., Antonoglou, I., Hubert, T., Simonyan, K., Sifre, L., Schmitt, S., … Silver, D. (2020). Mastering atari, go, chess and shogi by planning with a learned model. Nature, 588 (7839), 604–609
2020
Later among the works it cites.
Sutton, R. S., Machado, M. C., Holland, G. Z., Timbers, D. S. F., Tanner, B., White, A. (2022). Reward-respecting subtasks for model-based reinforcement learning. arXiv:2202.03466
Original
2022
Closest in time.

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