Computer Science > Artificial Intelligence
[Submitted on 29 Dec 2023 (v1), last revised 9 Mar 2024 (this version, v2)]
Title:State Machine of Thoughts: Leveraging Past Reasoning Trajectories for Enhancing Problem Solving
View PDF HTML (experimental)Abstract:Current Large Language Model-based agents reason within an exploration-evaluation framework, navigating problem-solving processes in a tree-like manner. However, these methods often neglect successful reasoning trajectories once a problem is resolved, leading to inefficient use of these trajectories for future analogous problems. To address this inefficiency, we adopt a state machine to record experience derived from previous reasoning trajectories. Within the state machine, states represent decomposed sub-problems, while state transitions reflect the dependencies among sub-problems. The state machine records both successful and failed trajectories. Utilizing the experience from the state machine, our proposed State Machine of Thoughts (SMoT) selects the most optimal sub-solutions and avoids incorrect ones. Our experiments show that SMoT can significantly improve problem-solving abilities in two exploration-intensive problems: the 24-point game and a taxi navigation reinforcement learning game.
Submission history
From: Jie Shuai [view email][v1] Fri, 29 Dec 2023 03:00:04 UTC (1,109 KB)
[v2] Sat, 9 Mar 2024 02:16:07 UTC (1,218 KB)
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