Combining LLMs with Logic-Based Framework to Explain MCTS (2505.00610v1)

Abstract: In response to the lack of trust in AI for sequential planning, we design a Computational Tree Logic-guided LLM-based natural language explanation framework designed for the Monte Carlo Tree Search (MCTS) algorithm. MCTS is often considered challenging to interpret due to the complexity of its search trees, but our framework is flexible enough to handle a wide range of free-form post-hoc queries and knowledge-based inquiries centered around MCTS and the Markov Decision Process (MDP) of the application domain. By transforming user queries into logic and variable statements, our framework ensures that the evidence obtained from the search tree remains factually consistent with the underlying environmental dynamics and any constraints in the actual stochastic control process. We evaluate the framework rigorously through quantitative assessments, where it demonstrates strong performance in terms of accuracy and factual consistency.

Combining LLMs with Logic-Based Framework to Explain MCTS

The paper "Combining LLMs with Logic-Based Framework to Explain MCTS" conducts an investigation into enhancing the explainability of Monte Carlo Tree Search (MCTS) algorithms through a synergy with LLMs guided by Computational Tree Logic (CTL). The primary objective is to mitigate trust concerns within the domain of AI utilized for sequential planning by creating a framework adept in offering explanations rooted in both logic and the underlying domain knowledge.

Overview

MCTS, known for its efficacy in handling complex sequential planning tasks across fields such as manufacturing engineering and transit route optimization, inherently poses interpretability challenges due to the complexity of its search trees. MCTS operates by simulating various paths within a search tree and selecting optimal actions based on statistical evaluations, making post-hoc explanations difficult without a deeper understanding of tree dynamics and stochastic decision processes. The authors aim to construct an explainable AI system capable of answering natural language inquiries related to MCTS and its domain MDPs by converting these queries into logical expressions and variable statements.

Framework Methodology

The presented framework utilizes an LLM approach integrated with CTL to facilitate two main types of user queries: post-hoc queries explaining completed plans, and background knowledge queries explaining MCTS processes more generally. The framework assesses these user queries by interpreting and categorizing them, subsequently transforming them into logic statements that can be evaluated against the MCTS search tree for factual consistency.

Key components of the framework include:

• Logic Generator and Parser: This translates user queries into logical formalism using predefined query types and hierarchical evidence structures which accommodate base-level, derived, and logic comparison evidence.
• Logic Scorer: Provides quantitative evaluations from MCTS trees using scorer functions that ascertain factual results against logical checks.
• Knowledge Retrieval: Employs retrieval-based generation (RAG) techniques, enhancing natural language explanations by embedding domain knowledge chunks within responses.

Responses are generated as narratives by the framework's Question-Answering LLM, which leverages both evaluated evidence and retrieved domain knowledge for a robust explanation.

Evaluation

The framework exhibits significant improvement in performance through rigorous quantitative evaluation against baseline LLM models, achieving marked advancements in both BERTScore and FactCC metrics. For instance, the framework showed a 2.40× enhancement using Llama3.1 and a 1.59× enhancement using the GPT-4 model in FactCC score, with notable improvements in factual consistency. This underscores the framework's capacity to generate more accurate and relevant explanations than traditional LLMs in the domain of MCTS explainability.

Implications and Future Work

The implications of this research are widespread in the field of AI planning and decision support systems. By enhancing transparency, accountability, and understandability of complex algorithms like MCTS, this framework sets the stage for broader acceptance and deployment of AI in critical real-world applications. Furthermore, the interactivity provided by the framework allows domain experts to probe deeper into the AI's reasoning, thus enabling better alignment of AI-generated decisions with human expectations and domain constraints.

Future explorations could explore applying the framework across different AI domains and algorithmic architectures, exploring more comprehensive ways to integrate logic and knowledge retrieval, potentially expanding into synthesis of domain-specific logic frameworks for other types of decision processes. Also, further development on dynamic, real-time querying techniques could refine the ability of the framework to act collaboratively within interactive environments, increasing its adaptability and scalability in diverse applications.