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Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems

作者： Oliver Deane Oliver Ray University of Bristol Bristol United Kingdom

ISBN: (纸本)9798400714269

In this paper, we introduce SYMPLEX (Symbolic Policy Learning from Experts/Exploration), an interactive framework that learns complex hierarchies of behavioral norms as interpretable logical constraints through a combination of autonomous exploration and expert imitation. The approach ensures that learned constraints are interpretable for human oversight, generalizable for transfer to similar environments, and defeasible - enabling adaptation to novel behaviors and facilitating the learning of exceptions in dynamic domains. We demonstrate the utility of our approach in a traffic simulation environment using a neuro-symbolic implementation of SYMPLEX that interleaves a Deep Q-Learning (DQL) component for policy optimization through goal-directed domain exploration, with interactive inductive logic programming (ILP) for example-based symbolic constraint generation. At each iteration, inferred constraints are imposed on the DQL via penalty terms appended to the reward function, allowing the system to form exceptions to previously-learned constraints. We illustrate SYMPLEX's ability to identify concise human-readable constraints in complex environments, and evidence the efficacy of learning norms as defeasible constraints. Additionally, we exemplify the benefits of using an interactive rule induction system in expediting convergence to accurate norms.

关键词： inductive logic programming

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Probabilistic Rule Induction for Transparent CBR Under Uncertainty 41st

Probabilistic Rule Induction for Transparent CBR Under Uncer...

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41st SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence (AI)

作者： Jedwabny, Martin Bisquert, Pierre Croitoru, Madalina Univ Montpellier CNRS INRIA LIRMM Montpellier France Univ Montpellier Inst Agro INRAE IATE Montpellier France

ISBN: (纸本)9783030911003;9783030910990

CBR systems leverage past experiences to make decisions. Recently, the AI community has taken an interest in making CBR systems explainable. logic-based frameworks make answers straightforward to explain. However, they struggle in the face of conflicting information, unlike probabilistic techniques. We show how probabilistic inductive logic programming (PILP) can be applied in CBR systems to make transparent decisions combining logic and probabilities. Then, we demonstrate how our approach can be applied in scenarios presenting uncertainty.

关键词： Case-based reasoning Probabilistic logic programming inductive logic programming Explanability

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Conceptual model visual simulation and the inductive learning of missing domain constraints

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DATA & KNOWLEDGE ENGINEERING 2022年 140卷

作者： Fumagalli, Mattia Sales, Tiago Prince Baiao, Fernanda Araujo Guizzardi, Giancarlo Free Univ Bozen Bolzano Conceptual & Cognit Modeling Res Grp CORE Bolzano Italy Pontif Catholic Univ Rio De Janeiro Dept Ind Engn Rio De Janeiro Brazil Univ Twente Serv & Cybersecur SCS Enschede Netherlands

Conceptual modeling plays a fundamental role in information systems engineering, and in data and systems interoperability. To play their role as instruments for domain modeling, conceptual models must contain the exact set of constraints that represent the worldview of the relevant domain stakeholders. However, as empirical results show, conceptual modelers are subject to cognitive limitations and biases and, hence, in practice, they systematically produce models that fall short in that respect. Moreover, automating the process of formally assessing conceptual models in this sense (i.e., model validation) is notoriously hard, mainly because the intended worldview at hand lies in the mind of these stakeholders. In this paper, we provide a novel approach to model validation and automated constraint learning that combines, on one hand, Model Finding via the visual simulation of that model's valid instances and, on the other hand, inductive logic programming techniques. In our approach, we properly channel the results produced by the application of a visual model finding technique as input to a learning process. We then show how the approach is able to support the modeler in identifying missing constraints from the original model. The approach is validated against a catalog of empirically elicited conceptual modeling anti-patterns. As we show here, the approach is able to support the automated learning of constraints that are needed to rectify a number of relevant anti-patterns in this catalog.

关键词： Conceptual modeling Constraints learning Model validation inductive learning inductive logic programming Model simulation

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A new relational Tri-training system with adaptive data editing for inductive logic programming

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KNOWLEDGE-BASED SYSTEMS 2012年 35卷 173-185页

作者： Li, Yanjuan Guo, Maozu Harbin Inst Technol Sch Comp Sci & Technol Harbin 150006 Peoples R China NE Forestry Univ Sch Informat & Comp Engn Harbin Peoples R China

Relational Tri-training (k-Tri-training for short), as a relational semi-supervised learning system, can effectively exploit unlabeled examples to improve the generalization ability. However, the R-Tri-training may also suffer from the common problem in traditional semi-supervised learning, i.e., the performance is usually not stable for the unlabeled examples often be wrongly labeled and accumulated during the iterative learning process. In this paper, a new Relational Tri-training system named ADE-R-Tri-training (R-Tri-training with Adaptive Data Editing) is proposed. Not only does it employ a specific data editing technique to identify and correct the examples possibly mislabeled throughout the co-labeling iterations, but it also takes an adaptive strategy to decide whether to trigger the editing operation according to different cases. The adaptive strategy consists of five pre-conditional theorems, all of which ensure the iterative reduction of classification error under PAC (Probably Approximately Correct) learning theory. Experiments on well-known benchmarks show that ADE-R-Tri-training can more effectively enhance the performance of the hypothesis learned than R-Tri-training. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Machine learning inductive logic programming Tri-training Relational Tri-training Data editing Adaptive strategy PAC learning

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Explanation as a Process: User-Centric Construction of Multi-level and Multi-modal Explanations 44th

Explanation as a Process: User-Centric Construction of Multi...

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44th German Conference on Artificial Intelligence (KI)

作者： Finzel, Bettina Tafler, David E. Scheele, Stephan Schmid, Ute Univ Bamberg Cognit Syst Bamberg Germany Fraunhofer Inst Integrated Circuits IIS Sensory Percept Analyt Erlangen Germany

ISBN: (纸本)9783030876265;9783030876258

In the last years, XAI research has mainly been concerned with developing new technical approaches to explain deep learning models. Just recent research has started to acknowledge the need to tailor explanations to different contexts and requirements of stakeholders. Explanations must not only suit developers of models, but also domain experts as well as end users. Thus, in order to satisfy different stakeholders, explanation methods need to be combined. While multi-modal explanations have been used to make model predictions more transparent, less research has focused on treating explanation as a process, where users can ask for information according to the level of understanding gained at a certain point in time. Consequently, an opportunity to explore explanations on different levels of abstraction should be provided besides multi-modal explanations. We present a process-based approach that combines multi-level and multi-modal explanations. The user can ask for textual explanations or visualizations through conversational interaction in a drill-down manner. We use inductive logic programming, an interpretable machine learning approach, to learn a comprehensible model. Further, we present an algorithm that creates an explanatory tree for each example for which a classifier decision is to be explained. The explanatory tree can be navigated by the user to get answers of different levels of detail. We provide a proof-of-concept implementation for concepts induced from a semantic net about living beings.

关键词： Multi-level explanations Multi-modal explanations Explanatory processes Semantic net inductive logic programming

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Learning logic Specifications for Soft Policy Guidance in POMCP 23

Learning Logic Specifications for Soft Policy Guidance in PO...

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Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems

作者： Giulio Mazzi Daniele Meli Alberto Castellini Alessandro Farinelli University of Verona Verona Italy

ISBN: (纸本)9781450394321

Partially Observable Monte Carlo Planning (POMCP) is an efficient solver for Partially Observable Markov Decision Processes (POMDPs). It allows scaling to large state spaces by computing an approximation of the optimal policy locally and online, using a Monte Carlo Tree Search based strategy. However, POMCP suffers from sparse reward function, namely, rewards achieved only when the final goal is reached, particularly in environments with large state spaces and long horizons. Recently, logic specifications have been integrated into POMCP to guide exploration and to satisfy safety requirements. However, such policy-related rules require manual definition by domain experts, especially in real-world scenarios. In this paper, we use inductive logic programming to learn logic specifications from traces of POMCP executions, i.e., sets of belief-action pairs generated by the planner. Specifically, we learn rules expressed in the paradigm of answer set programming. We then integrate them inside POMCP to provide soft policy bias toward promising actions. In the context of two benchmark scenarios, rocksample and battery, we show that the integration of learned rules from small task instances can improve performance with fewer Monte Carlo simulations and in larger task instances. We make our modified version of POMCP publicly available at https://***/GiuMaz/pomcp_***.

关键词： partially observable Markov decision processes answer set programming planning under uncertainty inductive logic programming explainable AI

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A process framework for inducing and explaining Datalog theories

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ADVANCES IN DATA ANALYSIS AND CLASSIFICATION 2020年第4期14卷 821-835页

作者： Gromowski, Mark Siebers, Michael Schmid, Ute Univ Bamberg Cognit Syst Weberei 5 D-96049 Bamberg Germany

With the increasing prevalence of Machine Learning in everyday life, a growing number of people will be provided with Machine-Learned assessments on a regular basis. We believe that human users interacting with systems based on Machine-Learned classifiers will demand and profit from the systems' decisions being explained in an approachable and comprehensive way. We developed a general process framework for logic-rule-based classifiers facilitating mutual exchange between system and user. The framework constitutes a guideline for how a system can apply inductive logic programming in order to provide comprehensive explanations for classification choices and empowering users to evaluate and correct the system's decisions. It also includes users' corrections being integrated into the system's core logic rules via retraining in order to increase the overall performance of the human-computer system. The framework suggests various forms of explanations-like natural language argumentations, near misses emphasizing unique characteristics, or image annotations-to be integrated into the system.

关键词： Machine learning inductive logic programming Interpretability Explanations Explainability

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logical reduction of metarules

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MACHINE LEARNING 2020年第7期109卷 1323-1369页

作者： Cropper, Andrew Tourret, Sophie Univ Oxford Oxford England Max Planck Inst Informat Saarbrucken Germany

Many forms of inductive logic programming (ILP) usemetarules, second-order Horn clauses, to define the structure of learnable programs and thus the hypothesis space. Deciding which metarules to use for a given learning task is a major open problem and is a trade-off between efficiency and expressivity: the hypothesis space grows given more metarules, so we wish to use fewer metarules, but if we use too few metarules then we lose expressivity. In this paper, we study whether fragments of metarules can be logically reduced to minimal finite subsets. We consider two traditional forms of logical reduction: subsumption and entailment. We also consider a new reduction technique calledderivation reduction, which is based on SLD-resolution. We compute reduced sets of metarules for fragments relevant to ILP and theoretically show whether these reduced sets are reductions for more general infinite fragments. We experimentally compare learning with reduced sets of metarules on three domains: Michalski trains, string transformations, and game rules. In general, derivation reduced sets of metarules outperform subsumption and entailment reduced sets, both in terms of predictive accuracies and learning times.

关键词： inductive logic programming Meta-interpretive learning logical reduction Program induction inductive programming

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inductive general game playing

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MACHINE LEARNING 2020年第7期109卷 1393-1434页

作者： Cropper, Andrew Evans, Richard Law, Mark Univ Oxford Oxford England Imperial Coll London London England

General game playing (GGP) is a framework for evaluating an agent's general intelligence across a wide range of tasks. In the GGP competition, an agent is given the rules of a game (described as a logic program) that it has never seen before. The task is for the agent to play the game, thus generating game traces. The winner of the GGP competition is the agent that gets the best total score over all the games. In this paper, we invert this task: a learner is given game traces and the task is to learn the rules that could produce the traces. This problem is central toinductive general game playing(IGGP). We introduce a technique that automatically generates IGGP tasks from GGP games. We introduce an IGGP dataset which contains traces from 50 diverse games, such asSudoku,Sokoban, andCheckers. We claim that IGGP is difficult for existing inductive logic programming (ILP) approaches. To support this claim, we evaluate existing ILP systems on our dataset. Our empirical results show that most of the games cannot be correctly learned by existing systems. The best performing system solves only 40% of the tasks perfectly. Our results suggest that IGGP poses many challenges to existing approaches. Furthermore, because we can automatically generate IGGP tasks from GGP games, our dataset will continue to grow with the GGP competition, as new games are added every year. We therefore think that the IGGP problem and dataset will be valuable for motivating and evaluating future research.

关键词： Program induction Program synthesis inductive logic programming General game playing

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Constructing generative logical models for optimisation problems using domain knowledge

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MACHINE LEARNING 2020年第7期109卷 1371-1392页

作者： Srinivasan, Ashwin Vig, Lovekesh Shroff, Gautam BITS Pilani Dept Comp Sci & Informat Syst Goa Campus Pilani Goa India TCS Res New Delhi India

In this paper we seek to identify data instances with a low value of some objective (or cost) function. Normally posed as optimisation problems, our interest is in problems that have the following characteristics: (a) optimal, or even near-optimal solutions are very rare;(b) it is expensive to obtain the value of the objective function for large numbers of data instances;and (c) there is domain knowledge in the form of experience, rules-of-thumb, constraints and the like, which is difficult to translate into the usual constraints for numerical optimisation procedures. Here we investigate the use of inductive logic programming (ILP) to construct models within a procedure that progressively attempts to increase the number of near-optimal solutions. Using ILP in this manner requires a change in focus from discriminatory models (the usual staple for ILP) to generative models. Using controlled datasets, we investigate the use of probability-sampling of solutions based on the estimated cost of clauses found using ILP. Specifically, we compare the results obtained against: (a) simple random sampling;and (b) generative deep network models that use a low-level encoding and automatically construct higher-level features. Our results suggest: (1) Against each of the alternatives, probability-sampling from ILP-constructed models contain more near-optimal solutions;(2) The key to the effectiveness of ILP-constructed models is the availability of domain knowledge. We also demonstrate the use of ILP in this manner on two real-world problems from the area of drug-design (predicting solubility and binding affinity), using domain knowledge of chemical ring structures and functional groups. Taken together, our results suggest that generative modelling using ILP can be very effective for optimisation problems where: (a) the number of training instances to be used is restricted, and (b) there is domain knowledge relevant to low-cost solutions.

关键词： Domain knowledge guided optimisation inductive logic programming Generative models

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