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Exploring Decision-Making in Neural Networks
This chapter examines the performance of recurrent neural networks in game-like environments and how additional thinking time impacts their problem-solving capabilities. It explores the development of intrinsic meta-strategies within the networks, particularly regarding pacing and resource management in decision-making. The discussion also highlights the complexities of planning behavior in AI, with a focus on goal-directed actions and the cognitive demands involved in executing strategies.