Inductive Learning of Declarative Domain-Specific Heuristics for ASP

• URL: http://arxiv.org/abs/2308.15863v1
• Date: Wed, 30 Aug 2023 08:55:17 GMT
• Title: Inductive Learning of Declarative Domain-Specific Heuristics for ASP
• Authors: Richard Comploi-Taupe (Siemens AG \"Osterreich, Vienna, Austria)
• Abstract summary: This paper presents a novel approach to the automatic learning of domain-specifics. We use Inductive Logic Programming (ILP) to learn domain-specifics from examples stemming from (near) answer sets of small but representative problem instances. Our experimental results indicate that the learneds can improve solving performance and solution quality when solving larger, harder instances of the same problem.
• Score: 1.0904219197219578
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Domain-specific heuristics are a crucial technique for the efficient solving of problems that are large or computationally hard. Answer Set Programming (ASP) systems support declarative specifications of domain-specific heuristics to improve solving performance. However, such heuristics must be invented manually so far. Inventing domain-specific heuristics for answer-set programs requires expertise with the domain under consideration and familiarity with ASP syntax, semantics, and solving technology. The process of inventing useful heuristics would highly profit from automatic support. This paper presents a novel approach to the automatic learning of such heuristics. We use Inductive Logic Programming (ILP) to learn declarative domain-specific heuristics from examples stemming from (near-)optimal answer sets of small but representative problem instances. Our experimental results indicate that the learned heuristics can improve solving performance and solution quality when solving larger, harder instances of the same problem.

Related papers

• Make LLMs better zero-shot reasoners: Structure-orientated autonomous reasoning [52.83539473110143]
  We introduce a novel structure-oriented analysis method to help Large Language Models (LLMs) better understand a question. To further improve the reliability in complex question-answering tasks, we propose a multi-agent reasoning system, Structure-oriented Autonomous Reasoning Agents (SARA) Extensive experiments verify the effectiveness of the proposed reasoning system. Surprisingly, in some cases, the system even surpasses few-shot methods.
  arXiv  Detail & Related papers  (2024-10-18T05:30:33Z)
• Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function Optimization [50.485788083202124]
  Reinforcement Learning (RL) plays a crucial role in aligning large language models with human preferences and improving their ability to perform complex tasks. We introduce Direct Q-function Optimization (DQO), which formulates the response generation process as a Markov Decision Process (MDP) and utilizes the soft actor-critic (SAC) framework to optimize a Q-function directly parameterized by the language model. Experimental results on two math problem-solving datasets, GSM8K and MATH, demonstrate that DQO outperforms previous methods, establishing it as a promising offline reinforcement learning approach for aligning language models.
  arXiv  Detail & Related papers  (2024-10-11T23:29:20Z)
• Learning Logic Specifications for Policy Guidance in POMDPs: an Inductive Logic Programming Approach [57.788675205519986]
  We learn high-quality traces from POMDP executions generated by any solver. We exploit data- and time-efficient Indu Logic Programming (ILP) to generate interpretable belief-based policy specifications. We show that learneds expressed in Answer Set Programming (ASP) yield performance superior to neural networks and similar to optimal handcrafted task-specifics within lower computational time.
  arXiv  Detail & Related papers  (2024-02-29T15:36:01Z)
• Large Language Models as Analogical Reasoners [155.9617224350088]
  Chain-of-thought (CoT) prompting for language models demonstrates impressive performance across reasoning tasks. We introduce a new prompting approach, analogical prompting, designed to automatically guide the reasoning process of large language models.
  arXiv  Detail & Related papers  (2023-10-03T00:57:26Z)
• When Do Program-of-Thoughts Work for Reasoning? [51.2699797837818]
  We propose complexity-impacted reasoning score (CIRS) to measure correlation between code and reasoning abilities. Specifically, we use the abstract syntax tree to encode the structural information and calculate logical complexity. Code will be integrated into the EasyInstruct framework at https://github.com/zjunlp/EasyInstruct.
  arXiv  Detail & Related papers  (2023-08-29T17:22:39Z)
• Specifying and Exploiting Non-Monotonic Domain-Specific Declarative Heuristics in Answer Set Programming [5.887969742827488]
  Domain-specifics are an essential technique for solving problems efficiently. Current approaches to integrate domain-specifics with Answer Set Programming (ASP) are unsatisfactory when dealing with domains that are specified non-monotonically. We present novel syntax and semantics for declarative specifications of domain-specifics in ASP.
  arXiv  Detail & Related papers  (2022-09-19T14:57:50Z)
• Tools and Methodologies for Verifying Answer Set Programs [0.0]
  ASP is a powerful declarative programming paradigm commonly used for solving challenging search and optimization problems. As an approach to Knowledge Representation and Reasoning, ASP benefits from its simplicity, conciseness and rigorously defined semantics. My research is concerned with extending the theory and tools supporting the verification of ASP progams.
  arXiv  Detail & Related papers  (2022-08-05T10:50:21Z)
• Unifying Framework for Optimizations in non-boolean Formalisms [0.6853165736531939]
  Many popular automated reasoning paradigms provide languages supporting optimization statements. Here we propose a unifying framework that eliminates syntactic distinctions between paradigms. We study formal properties of the proposed systems that translate into formal properties of paradigms that can be captured within our framework.
  arXiv  Detail & Related papers  (2022-06-16T00:38:19Z)
• An Abstract View on Optimizations in Propositional Frameworks [0.6853165736531939]
  We propose a unifying framework of so-called weight systems that eliminates syntactic distinctions between paradigms. This framework has significant simplifying and explanatory potential in the studies of optimization and modularity in automated reasoning and knowledge representation.
  arXiv  Detail & Related papers  (2022-06-13T19:44:01Z)
• On the Configuration of More and Less Expressive Logic Programs [11.331373810571993]
  We consider two well-known model-based AI methodologies, SAT and ASP, define a number of syntactic features that may characterise their inputs. Results of a wide experimental analysis involving SAT and ASP domains, taken from respective competitions, show the different advantages that can be obtained by using input reformulation and configuration.
  arXiv  Detail & Related papers  (2022-03-02T10:55:35Z)
• Multi-Agent Reinforcement Learning with Temporal Logic Specifications [65.79056365594654]
  We study the problem of learning to satisfy temporal logic specifications with a group of agents in an unknown environment. We develop the first multi-agent reinforcement learning technique for temporal logic specifications. We provide correctness and convergence guarantees for our main algorithm.
  arXiv  Detail & Related papers  (2021-02-01T01:13:03Z)

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.