Papers Inductive logic programming
“Inductive logic programming” 태그가 달린 논문 181편 · 필터 해제
Relational decomposition for program synthesis
We introduce a novel approach to program synthesis that decomposes complex functional tasks into simpler relational synthesis sub-tasks. We demonstrate the effectiveness of our approach using an off-the-shelf inductive l…
Inductive logic programmingProgram SynthesisTowards Probabilistic Inductive Logic Programming with Neurosymbolic Inference and Relaxation
Many inductive logic programming (ILP) methods are incapable of learning programs from probabilistic background knowledge, e.g. coming from sensory data or neural networks with probabilities. We propose Propper, which ha…
Graph Neural NetworkInductive logic programmingInductive Learning of Logical Theories with LLMs: An Expressivity-Graded Analysis
This work presents a novel systematic methodology to analyse the capabilities and limitations of Large Language Models (LLMs) with feedback from a formal inference engine, on logic theory induction. The analysis is compl…
Inductive LearningInductive logic programmingNeurosymbolic Methods for Rule Mining
In this chapter, we address the problem of rule mining, beginning with essential background information, including measures of rule quality. We then explore various rule mining methodologies, categorized into three group…
Inductive logic programmingAlgebraic anti-unification
Abstraction is key to human and artificial intelligence as it allows one to see common structure in otherwise distinct objects or situations and as such it is a key element for generality in AI. Anti-unification (or gene…
Inductive logic programmingAutoCoder: Enhancing Code Large Language Model with \textsc{AIEV-Instruct}
We introduce AutoCoder, the first Large Language Model to surpass GPT-4 Turbo (April 2024) and GPT-4o in pass@1 on the Human Eval benchmark test ($\mathbf{90.9\%}$ vs. $\mathbf{90.2\%}$). In addition, AutoCoder offers a …
Class-level Code GenerationCode CompletionCode GenerationCode Repair+8Program Synthesis using Inductive Logic Programming for the Abstraction and Reasoning Corpus
The Abstraction and Reasoning Corpus (ARC) is a general artificial intelligence benchmark that is currently unsolvable by any Machine Learning method, including Large Language Models (LLMs). It demands strong generalizat…
ARCInductive logic programmingProgram SynthesisLearning Logic Specifications for Policy Guidance in POMDPs: an Inductive Logic Programming Approach
Partially Observable Markov Decision Processes (POMDPs) are a powerful framework for planning under uncertainty. They allow to model state uncertainty as a belief probability distribution. Approximate solvers based on Mo…
Inductive logic programmingLearning big logical rules by joining small rules
A major challenge in inductive logic programming is learning big rules. To address this challenge, we introduce an approach where we join small rules to learn big rules. We implement our approach in a constraint-driven s…
Drug DesignInductive logic programmingLearning logic programs by finding minimal unsatisfiable subprograms
The goal of inductive logic programming (ILP) is to search for a logic program that generalises training examples and background knowledge. We introduce an ILP approach that identifies minimal unsatisfiable subprograms (…
Inductive logic programmingProgram SynthesisLLMs for Relational Reasoning: How Far are We?
Large language models (LLMs) have revolutionized many areas (e.g. natural language processing, software engineering, etc.) by achieving state-of-the-art performance on extensive downstream tasks. Aiming to achieve robust…
Common Sense ReasoningDecision MakingInductive logic programmingProgram induction+2A Unifying Framework for Learning Argumentation Semantics
Argumentation is a very active research field of Artificial Intelligence concerned with the representation and evaluation of arguments used in dialogues between humans and/or artificial agents. Acceptability semantics of…
Inductive logic programmingSymbolic Imitation Learning: From Black-Box to Explainable Driving Policies
Current methods of imitation learning (IL), primarily based on deep neural networks, offer efficient means for obtaining driving policies from real-world data but suffer from significant limitations in interpretability a…
Autonomous DrivingImitation LearningInductive logic programming"Would life be more interesting if I were in AI?" Answering Counterfactuals based on Probabilistic Inductive Logic Programming
Probabilistic logic programs are logic programs where some facts hold with a specified probability. Here, we investigate these programs with a causal framework that allows counterfactual queries. Learning the program str…
counterfactualCounterfactual ReasoningHeuristic SearchInductive logic programmingInductive Learning of Declarative Domain-Specific Heuristics for ASP
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 …
Inductive LearningInductive logic programmingTowards One-Shot Learning for Text Classification using Inductive Logic Programming
With the ever-increasing potential of AI to perform personalised tasks, it is becoming essential to develop new machine learning techniques which are data-efficient and do not require hundreds or thousands of training da…
ClassificationCommon Sense ReasoningInductive logic programmingOne-Shot Learning+2Deep Inductive Logic Programming meets Reinforcement Learning
One approach to explaining the hierarchical levels of understanding within a machine learning model is the symbolic method of inductive logic programming (ILP), which is data efficient and capable of learning first-order…
Inductive logic programmingreinforcement-learningReinforcement LearningExplainable and Trustworthy Traffic Sign Detection for Safe Autonomous Driving: An Inductive Logic Programming Approach
Traffic sign detection is a critical task in the operation of Autonomous Vehicles (AV), as it ensures the safety of all road users. Current DNN-based sign classification systems rely on pixel-level features to detect tra…
Autonomous DrivingAutonomous VehiclesInductive logic programmingTraffic Sign DetectionHuman Comprehensible Active Learning of Genome-Scale Metabolic Networks
An important application of Synthetic Biology is the engineering of the host cell system to yield useful products. However, an increase in the scale of the host system leads to huge design space and requires a large numb…
Active LearningExperimental DesignInductive logic programmingLogical ReasoningLearning MDL logic programs from noisy data
Many inductive logic programming approaches struggle to learn programs from noisy data. To overcome this limitation, we introduce an approach that learns minimal description length programs from noisy data, including rec…
Drug DesignInductive logic programmingProgram Synthesis