Agent-Driven Rewriting: Methods & Applications

• Agent-driven rewriting is a technique that uses automated agents and AI to transform code, queries, and data for improved computational efficiency.
• It employs methods like machine learning, reinforcement learning, and rule-based decision-making to optimize complex tasks in various domains.
• Applications span from ASP logic optimization and query rewriting in advertising to dynamic routing in logistics and error correction in conversational AI.

Agent-driven rewriting is a method of transforming code, queries, or textual data using agents – automated systems designed to perform tasks that usually require human intervention. These agents can analyze, decide, and modify the input based on specific rules, algorithms, or learned behaviors. By leveraging artificial intelligence, especially machine learning models and heuristics, agent-driven rewriting can enhance performance, adaptability, and efficiency in computational tasks. This article presents a comprehensive overview of agent-driven rewriting, focusing on its key methodologies, applications, and experimental findings across various domains.

1. Machine Learning and ASP Logic Programs

The implementation of machine learning in agent-driven rewriting is exemplified in the context of Answer Set Programming (ASP). The method involves using a Multilayer Perceptron (MLP) to predict when rewriting an ASP rule would be beneficial. By examining six specific features, such as the number of joins and the average arity of predicates, the MLP determines if a rule should be decomposed, left unchanged, or rewritten. This predictive rewriting method can streamline ASP systems by minimizing the grounding bottleneck and improving solving stages.

2. FGL Rewriter: Extended Rewrite Capabilities

The FGL rewriter builds on previous rewriting frameworks by incorporating features that allow more nuanced and precise code transformations. Key capabilities include the use of precise contexts, abort-rewriting for failed transformations, and advanced binder rules for free variables. By using these enhancements, the FGL rewriter allows for agent-like decision-making processes that optimize rewriting paths for logic programs, making operations more flexible and reducing computational overhead.

3. RP-Rewriter for Large Terms

The RP-Rewriter focuses on efficiently managing and transforming large logical terms in ACL2 using novel features like side conditions. These features enable the delayed evaluation of properties during rewriting, effectively retaining essential information without mandating extensive backchaining. This agent-like system showcases how meta-rules and data structures can efficiently handle complex rewriting tasks while avoiding redundancy and excessive resource use.

4. Conversational AI and Query Rewriting

In the conversational AI domain, agent-driven rewriting is applied to enhance error-prone Automatic Speech Recognition (ASR) outputs. By leveraging user-specific historical interactions stored as memories, this system uses neural retrieval models and pointer-generator networks to refine ASR hypotheses, increasing semantic and structural diversity. The combination of retrieval and generation strategies allows for better recovery of user intents, leading to significant improvements in SLU performance.

5. Diversity-driven Query Rewriting in Advertising

CLOVER, a framework used in search advertising, employs diversity-driven reinforcement learning to generate high-quality and diverse query rewrites. By aligning rewriting tasks with human-judged quality metrics, CLOVER successfully combines the capability of neural generation models with the robust evaluation of diverse outputs. The system improves ad targeting by generating keywords that capture multiple semantic variations, demonstrating enhanced user engagement and revenue gains in experimental studies.

6. Multi-Agent Neural Rewriter for Routing Problems

The multi-agent neural rewriter addresses the vehicle routing problem by iteratively rewriting routes through localized modifications. Each agent independently optimizes its component of the solution, utilizing partial observability to maintain privacy while achieving near-optimal routing solutions. This system demonstrates how decentralized decision-making can improve logistic efficiency, closely matching benchmarks even when only partial cost data is disclosed.

7. Rewriting the Infinite Chase

Addressing the infinite chase problem with guarded tuple-generating dependencies (GTGDs), this approach involves rewriting rules to finite Datalog programs to handle complex logical dependencies efficiently. The resulting rewrite system captures infinite logical derivations as finite programs, significantly enhancing the efficiency of reasoning tasks in information systems, thus enabling autonomous, agent-driven logic management and query answering.

In summary, agent-driven rewriting employs advanced AI techniques to transform inputs dynamically and intelligently, enhancing the performance, adaptability, and robustness of various computational systems. With applications ranging from ASP logic optimization to relational query improvement and conversational AI enhancements, these methodologies represent a significant step forward in automating complex rewriting tasks across domains.