Self-Guiding Exploration for Combinatorial Problems

• URL: http://arxiv.org/abs/2405.17950v1
• Date: Tue, 28 May 2024 08:26:54 GMT
• Title: Self-Guiding Exploration for Combinatorial Problems
• Authors: Zangir Iklassov, Yali Du, Farkhad Akimov, Martin Takac,
• Abstract summary: Self-Guiding Exploration (SGE) is designed to enhance the performance of solving Combinatorial Problems. SGE operates autonomously, generating multiple thought trajectories for each CP task. It then breaks these trajectories down into actionable subtasks, executes them sequentially, and refines the results to ensure optimal outcomes.
• Score: 2.636330943305939
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have become pivotal in addressing reasoning tasks across diverse domains, including arithmetic, commonsense, and symbolic reasoning. They utilize prompting techniques such as Exploration-of-Thought, Decomposition, and Refinement to effectively navigate and solve intricate tasks. Despite these advancements, the application of LLMs to Combinatorial Problems (CPs), known for their NP-hardness and critical roles in logistics and resource management remains underexplored. To address this gap, we introduce a novel prompting strategy: Self-Guiding Exploration (SGE), designed to enhance the performance of solving CPs. SGE operates autonomously, generating multiple thought trajectories for each CP task. It then breaks these trajectories down into actionable subtasks, executes them sequentially, and refines the results to ensure optimal outcomes. We present our research as the first to apply LLMs to a broad range of CPs and demonstrate that SGE outperforms existing prompting strategies by over 27.84% in CP optimization performance. Additionally, SGE achieves a 2.46% higher accuracy over the best existing results in other reasoning tasks (arithmetic, commonsense, and symbolic).

Related papers

• Guiding Multi-agent Multi-task Reinforcement Learning by a Hierarchical Framework with Logical Reward Shaping [16.5526277899717]
  This study aims to design a multi-agent cooperative algorithm with logic reward shaping. Experiments have been conducted on various types of tasks in the Minecraft-like environment.
  arXiv  Detail & Related papers  (2024-11-02T09:03:23Z)
• Sample-Efficient Reinforcement Learning with Temporal Logic Objectives: Leveraging the Task Specification to Guide Exploration [13.053013407015628]
  This paper addresses the problem of learning optimal control policies for systems with uncertain dynamics. We propose an accelerated RL algorithm that can learn control policies significantly faster than competitive approaches.
  arXiv  Detail & Related papers  (2024-10-16T00:53:41Z)
• EVOLvE: Evaluating and Optimizing LLMs For Exploration [76.66831821738927]
  Large language models (LLMs) remain under-studied in scenarios requiring optimal decision-making under uncertainty. We measure LLMs' (in)ability to make optimal decisions in bandits, a state-less reinforcement learning setting relevant to many applications. Motivated by the existence of optimal exploration algorithms, we propose efficient ways to integrate this algorithmic knowledge into LLMs.
  arXiv  Detail & Related papers  (2024-10-08T17:54:03Z)
• Meta Reasoning for Large Language Models [58.87183757029041]
  We introduce Meta-Reasoning Prompting (MRP), a novel and efficient system prompting method for large language models (LLMs) MRP guides LLMs to dynamically select and apply different reasoning methods based on the specific requirements of each task. We evaluate the effectiveness of MRP through comprehensive benchmarks.
  arXiv  Detail & Related papers  (2024-06-17T16:14:11Z)
• Plan of Thoughts: Heuristic-Guided Problem Solving with Large Language Models [0.0]
  We formalize a planning-based approach to perform multi-step problem solving with language models. We demonstrate a superior success rate of 89.4% on the Game of 24 task as compared to existing approaches.
  arXiv  Detail & Related papers  (2024-04-29T18:51:17Z)
• An Examination on the Effectiveness of Divide-and-Conquer Prompting in Large Language Models [28.139780691709266]
  We provide a theoretic analysis to divide-and-conquer prompting strategy and help us identify the specific tasks where DaC prompting can bring performance boost with theoretic guarantee. We present two cases (large integer arithmetic and fact verification) where experimental results align with our theoretic analysis.
  arXiv  Detail & Related papers  (2024-02-08T02:37:30Z)
• StrategyLLM: Large Language Models as Strategy Generators, Executors, Optimizers, and Evaluators for Problem Solving [76.5322280307861]
  StrategyLLM allows LLMs to perform inductive reasoning, deriving general strategies from specific task instances, and deductive reasoning, applying these general strategies to particular task examples, for constructing generalizable and consistent few-shot prompts. Experimental results demonstrate that StrategyLLM outperforms the competitive baseline CoT-SC that requires human-annotated solutions on 13 datasets across 4 challenging tasks without human involvement, including math reasoning (34.2% $rightarrow$ 38.8%), commonsense reasoning (70.3% $rightarrow$ 72.5%), algorithmic reasoning (73.7% $rightarrow$ 85.0
  arXiv  Detail & Related papers  (2023-11-15T09:18:09Z)
• Provable Benefits of Multi-task RL under Non-Markovian Decision Making Processes [56.714690083118406]
  In multi-task reinforcement learning (RL) under Markov decision processes (MDPs), the presence of shared latent structures has been shown to yield significant benefits to the sample efficiency compared to single-task RL. We investigate whether such a benefit can extend to more general sequential decision making problems, such as partially observable MDPs (POMDPs) and more general predictive state representations (PSRs) We propose a provably efficient algorithm UMT-PSR for finding near-optimal policies for all PSRs, and demonstrate that the advantage of multi-task learning manifests if the joint model class of PSR
  arXiv  Detail & Related papers  (2023-10-20T14:50:28Z)
• Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning [56.26889258704261]
  We propose a novel "disentangled" decision-making method, Semantically Aligned task decomposition in MARL (SAMA) SAMA prompts pretrained language models with chain-of-thought that can suggest potential goals, provide suitable goal decomposition and subgoal allocation as well as self-reflection-based replanning. SAMA demonstrates considerable advantages in sample efficiency compared to state-of-the-art ASG methods.
  arXiv  Detail & Related papers  (2023-05-18T10:37:54Z)
• Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning [11.91425153754564]
  We show that in environments with a highly multi-modal reward landscape, value decomposition, and parameter sharing can be problematic and lead to undesired outcomes. In contrast, policy gradient (PG) methods with individual policies provably converge to an optimal solution in these cases. We present practical suggestions on implementing multi-agent PG algorithms for either high rewards or diverse emergent behaviors.
  arXiv  Detail & Related papers  (2022-06-15T13:03:05Z)
• Meta Reinforcement Learning with Autonomous Inference of Subtask Dependencies [57.27944046925876]
  We propose and address a novel few-shot RL problem, where a task is characterized by a subtask graph. Instead of directly learning a meta-policy, we develop a Meta-learner with Subtask Graph Inference. Our experiment results on two grid-world domains and StarCraft II environments show that the proposed method is able to accurately infer the latent task parameter.
  arXiv  Detail & Related papers  (2020-01-01T17:34:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.