Artificial Intelligence最新文献

{"title":"Gödel–Dummett linear temporal logic","authors":"Juan Pablo Aguilera ,&nbsp;Martín Diéguez ,&nbsp;David Fernández-Duque ,&nbsp;Brett McLean","doi":"10.1016/j.artint.2024.104236","DOIUrl":"10.1016/j.artint.2024.104236","url":null,"abstract":"<div><div>We investigate a version of linear temporal logic whose propositional fragment is Gödel–Dummett logic (which is well known both as a superintuitionistic logic and a t-norm fuzzy logic). We define the logic using two natural semantics: first a real-valued semantics, where statements have a degree of truth in the real unit interval, and second a ‘bi-relational’ semantics. We then show that these two semantics indeed define one and the same logic: the statements that are valid for the real-valued semantics are the same as those that are valid for the bi-relational semantics. This Gödel temporal logic does not have any form of the finite model property for these two semantics: there are non-valid statements that can only be falsified on an infinite model. However, by using the technical notion of a quasimodel, we show that every falsifiable statement is falsifiable on a finite quasimodel, yielding an algorithm for deciding if a statement is valid or not. Later, we strengthen this decidability result by giving an algorithm that uses only a polynomial amount of memory, proving that Gödel temporal logic is PSPACE-complete. We also provide a deductive calculus for Gödel temporal logic, and show this calculus to be sound and complete for the above-mentioned semantics, so that all (and only) the valid statements can be proved with this calculus.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"338 ","pages":"Article 104236"},"PeriodicalIF":5.1,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142577891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowing how to plan about planning: Higher-order and meta-level epistemic planning","authors":"Yanjun Li ,&nbsp;Yanjing Wang","doi":"10.1016/j.artint.2024.104233","DOIUrl":"10.1016/j.artint.2024.104233","url":null,"abstract":"<div><div>Automated planning in AI and the logics of knowing how have close connections. In the recent literature, various <em>planning-based know-how logics</em> have been proposed and studied, making use of several notions of planning in AI. In this paper, we explore the <em>reverse</em> direction by using a multi-agent logic of knowing how to do <em>know-how-based planning</em> via model checking and theorem proving/satisfiability checking. Based on our logical framework, we propose two new classes of related planning problems: <em>higher-order epistemic planning</em> and <em>meta-level epistemic planning</em>, which generalize the current genre of epistemic planning in the literature. The former is for planning about planning, i.e., planning with higher-order goals that are again about epistemic planning, e.g., finding a plan for an agent to make sure <em>p</em> such that the adversary does not know how to make <em>p</em> false in the future. The latter is about planning at the meta-level by abstract reasoning combining knowledge-how from different agents, e.g., given that <em>i</em> knows how to prove a lemma and <em>i</em> knows <em>j</em> knows how to prove the theorem once the lemma is proved, we should derive that <em>i</em> knows how to let <em>j</em> knows how to prove the theorem. To make these possible, our framework features not only the operators of know-that and know-how but also a temporal operator □, which can help in capturing both the <em>local</em> and <em>global</em> knowledge-how. We axiomatize this powerful logic over finite models with perfect recall and show its decidability. We also give a PTIME algorithm for the model checking problem over finite models.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"337 ","pages":"Article 104233"},"PeriodicalIF":5.1,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142527870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatically designing counterfactual regret minimization algorithms for solving imperfect-information games","authors":"Kai Li ,&nbsp;Hang Xu ,&nbsp;Haobo Fu ,&nbsp;Qiang Fu ,&nbsp;Junliang Xing","doi":"10.1016/j.artint.2024.104232","DOIUrl":"10.1016/j.artint.2024.104232","url":null,"abstract":"<div><div>Strategic decision-making in imperfect-information games is an important problem in artificial intelligence. Counterfactual regret minimization (CFR), a family of iterative algorithms, has been the workhorse for solving these types of games since its inception. In recent years, a series of novel CFR variants have been proposed, significantly improving the convergence rate of vanilla CFR. However, most of these new variants are hand-designed by researchers through trial and error, often based on different motivations, which generally requires a tremendous amount of effort and insight. This work proposes AutoCFR, a systematic framework that meta-learns novel CFR algorithms through evolution, easing the burden of manual algorithm design. We first design a search language that is rich enough to represent various CFR variants. We then exploit a scalable regularized evolution algorithm with a set of acceleration techniques to efficiently search over the combinatorial space of algorithms defined by this language. The learned novel CFR algorithm can generalize to new imperfect-information games not seen during training and performs on par with or better than existing state-of-the-art CFR variants. In addition to superior empirical performance, we also theoretically show that the learned algorithm converges to an approximate Nash equilibrium. Extensive experiments across diverse imperfect-information games highlight the scalability, extensibility, and generalizability of AutoCFR, establishing it as a general-purpose framework for solving imperfect-information games.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"337 ","pages":"Article 104232"},"PeriodicalIF":5.1,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142527869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Declarative probabilistic logic programming in discrete-continuous domains","authors":"Pedro Zuidberg Dos Martires ,&nbsp;Luc De Raedt ,&nbsp;Angelika Kimmig","doi":"10.1016/j.artint.2024.104227","DOIUrl":"10.1016/j.artint.2024.104227","url":null,"abstract":"<div><div>Over the past three decades, the logic programming paradigm has been successfully expanded to support probabilistic modeling, inference and learning. The resulting paradigm of probabilistic logic programming (PLP) and its programming languages owes much of its success to a declarative semantics, the so-called distribution semantics. However, the distribution semantics is limited to discrete random variables only. While PLP has been extended in various ways for supporting hybrid, that is, mixed discrete and continuous random variables, we are still lacking a declarative semantics for hybrid PLP that not only generalizes the distribution semantics and the modeling language but also the standard inference algorithm that is based on knowledge compilation. We contribute the <em>measure semantics</em> together with the hybrid PLP language DC-ProbLog (where DC stands for distributional clauses) and its inference engine <em>infinitesimal algebraic likelihood weighting</em> (IALW). These have the original distribution semantics, standard PLP languages such as ProbLog, and standard inference engines for PLP based on knowledge compilation as special cases. Thus, we generalize the state of the art of PLP towards hybrid PLP in three different aspects: semantics, language and inference. Furthermore, IALW is the first inference algorithm for hybrid probabilistic programming based on knowledge compilation.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"337 ","pages":"Article 104227"},"PeriodicalIF":5.1,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An α-regret analysis of adversarial bilateral trade","authors":"Yossi Azar ,&nbsp;Amos Fiat ,&nbsp;Federico Fusco","doi":"10.1016/j.artint.2024.104231","DOIUrl":"10.1016/j.artint.2024.104231","url":null,"abstract":"<div><div>We study sequential bilateral trade where sellers and buyers valuations are completely arbitrary (<em>i.e.</em>, determined by an adversary). Sellers and buyers are strategic agents with private valuations for the good and the goal is to design a mechanism that maximizes efficiency (or gain from trade) while being incentive compatible, individually rational and budget balanced. In this paper we consider gain from trade, which is harder to approximate than social welfare.</div><div>We consider a variety of feedback scenarios and distinguish the cases where the mechanism posts one price and when it can post different prices for buyer and seller. We show several surprising results about the separation between the different scenarios. In particular we show that (a) it is impossible to achieve sublinear <em>α</em>-regret for any <span><math><mi>α</mi><mo>&lt;</mo><mn>2</mn></math></span>, (b) but with full feedback sublinear 2-regret is achievable; (c) with a single price and partial feedback one cannot get sublinear <em>α</em> regret for any constant <em>α</em> (d) nevertheless, posting two prices even with one-bit feedback achieves sublinear 2-regret, and (e) there is a provable separation in the 2-regret bounds between full and partial feedback.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"337 ","pages":"Article 104231"},"PeriodicalIF":5.1,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On trivalent logics, probabilistic weak deduction theorems, and a general import-export principle","authors":"Angelo Gilio ,&nbsp;David E. Over ,&nbsp;Niki Pfeifer ,&nbsp;Giuseppe Sanfilippo","doi":"10.1016/j.artint.2024.104229","DOIUrl":"10.1016/j.artint.2024.104229","url":null,"abstract":"<div><div>In this paper we first recall some results for conditional events, compound conditionals, conditional random quantities, p-consistency, and p-entailment. We discuss the equivalence between conditional bets and bets on conditionals, and review de Finetti's trivalent analysis of conditionals. But we go beyond de Finetti's early trivalent logical analysis and his later ideas, aiming to take his proposals to a higher level. We examine two recent articles that explore trivalent logics for conditionals and their definitions of logical validity and compare them with the approach to compound conditionals introduced by Gilio and Sanfilippo within the framework of conditional random quantities. As we use the notion of p-entailment, the full deduction theorem does not hold. We prove a Probabilistic Weak Deduction Theorem for conditional events. After that we study some variants of it, with further results, and we present several examples. Moreover, we illustrate how to derive new inference rules related to selected Aristotelian syllogisms. We focus on iterated conditionals and the invalidity of the Import-Export principle in the light of our Probabilistic Weak Deduction Theorem. We use the inference from a disjunction, <em>A or B</em>, to the conditional, <em>if not-A then B</em>, as an example to show the invalidity of this principle. We introduce a General Import-Export principle by examining examples and counterexamples. In particular, when considering the inference rules of System P, we find that a General Import-Export principle is satisfied, even if the assumptions of the Probabilistic Weak Deduction Theorem do not hold. We also deepen further aspects related to p-entailment and p-consistency. Finally, we briefly discuss some related work relevant to AI.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"337 ","pages":"Article 104229"},"PeriodicalIF":5.1,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142328068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive large-neighbourhood search for optimisation in answer-set programming","authors":"Thomas Eiter ,&nbsp;Tobias Geibinger ,&nbsp;Nelson Higuera Ruiz ,&nbsp;Nysret Musliu ,&nbsp;Johannes Oetsch ,&nbsp;Dave Pfliegler ,&nbsp;Daria Stepanova","doi":"10.1016/j.artint.2024.104230","DOIUrl":"10.1016/j.artint.2024.104230","url":null,"abstract":"<div><div>Answer-set programming (ASP) is a prominent approach to declarative problem solving that is increasingly used to tackle challenging optimisation problems. We present an approach to leverage ASP optimisation by using large-neighbourhood search (LNS), which is a meta-heuristic where parts of a solution are iteratively destroyed and reconstructed in an attempt to improve an overall objective. In our LNS framework, neighbourhoods can be specified either declaratively as part of the ASP encoding or automatically generated by code. Furthermore, our framework is self-adaptive, i.e., it also incorporates portfolios for the LNS operators along with selection strategies to adjust search parameters on the fly. The implementation of our framework, the system ALASPO, currently supports the ASP solver clingo, as well as its extensions clingo-dl and clingcon that allow for difference and full integer constraints, respectively. It utilises multi-shot solving to efficiently realise the LNS loop and in this way avoids program regrounding. We describe our LNS framework for ASP as well as its implementation, discuss methodological aspects, and demonstrate the effectiveness of the adaptive LNS approach for ASP on different optimisation benchmarks, some of which are notoriously difficult, as well as real-world applications for shift planning, configuration of railway-safety systems, parallel machine scheduling, and test laboratory scheduling.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"337 ","pages":"Article 104230"},"PeriodicalIF":5.1,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0004370224001668/pdfft?md5=ab34b67efbd10758275677814fac17d7&pid=1-s2.0-S0004370224001668-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142316277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integration of memory systems supporting non-symbolic representations in an architecture for lifelong development of artificial agents","authors":"François Suro,&nbsp;Fabien Michel,&nbsp;Tiberiu Stratulat","doi":"10.1016/j.artint.2024.104228","DOIUrl":"10.1016/j.artint.2024.104228","url":null,"abstract":"<div><p>Compared to autonomous agent learning, lifelong agent learning tackles the additional challenge of accumulating skills in a way favourable to long term development. What an agent learns at a given moment can be an element for the future creation of behaviours of greater complexity, whose purpose cannot be anticipated.</p><p>Beyond its initial low-level sensorimotor development phase, the agent is expected to acquire, in the same manner as skills, values and goals which support the development of complex behaviours beyond the reactive level. To do so, it must have a way to represent and memorize such information.</p><p>In this article, we identify the properties suitable for a representation system supporting the lifelong development of agents through a review of a wide range of memory systems and related literature. Following this analysis, our second contribution is the proposition and implementation of such a representation system in MIND, a modular architecture for lifelong development. The new <em>variable module</em> acts as a simple memory system which is strongly integrated to the hierarchies of skill modules of MIND, and allows for the progressive structuration of behaviour around persistent non-symbolic representations. <em>Variable modules</em> have many applications for the development and structuration of complex behaviours, but also offer designers and operators explicit models of values and goals facilitating human interaction, control and explainability.</p><p>We show through experiments two possible uses of <em>variable modules</em>. In the first experiment, skills exchange information by using a variable representing the concept of “target”, which allows the generalization of navigation behaviours. In the second experiment, we show how a non-symbolic representation can be learned and memorized to develop beyond simple reactive behaviour, and keep track of the steps of a process whose state cannot be inferred by observing the environment.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"337 ","pages":"Article 104228"},"PeriodicalIF":5.1,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0004370224001644/pdfft?md5=6e7585d9f3b58a33fd4b372451648a7b&pid=1-s2.0-S0004370224001644-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PathLAD+: Towards effective exact methods for subgraph isomorphism problem","authors":"Yiyuan Wang ,&nbsp;Chenghou Jin ,&nbsp;Shaowei Cai","doi":"10.1016/j.artint.2024.104219","DOIUrl":"10.1016/j.artint.2024.104219","url":null,"abstract":"<div><p>The subgraph isomorphism problem (SIP) is a challenging problem with wide practical applications. In the last decade, despite being a theoretical hard problem, researchers designed various algorithms for solving SIP. In this work, we propose five main strategies and develop an improved exact algorithm for SIP. First, we design a probing search procedure to try whether the search procedure can successfully obtain a solution at first sight. Second, we design a novel matching ordering strategy as a value-ordering heuristic, which uses some useful information obtained from the probing search procedure to preferentially select some promising target vertices. Third, we discuss the characteristics of different propagation methods in the context of SIP and present an adaptive propagation method to make a good balance between these methods. Moreover, to further improve the performance of solving large graphs, we propose an enhanced implementation of the edge constraint method and a domain limitation strategy, which aims to accelerate the search process. Experimental results on a broad range of classic and graph-database benchmarks show that our proposed algorithm performs better than several state-of-the-art algorithms for the SIP.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"337 ","pages":"Article 104219"},"PeriodicalIF":5.1,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142230639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interval abstractions for robust counterfactual explanations","authors":"Junqi Jiang,&nbsp;Francesco Leofante,&nbsp;Antonio Rago,&nbsp;Francesca Toni","doi":"10.1016/j.artint.2024.104218","DOIUrl":"10.1016/j.artint.2024.104218","url":null,"abstract":"<div><p>Counterfactual Explanations (CEs) have emerged as a major paradigm in explainable AI research, providing recourse recommendations for users affected by the decisions of machine learning models. However, CEs found by existing methods often become invalid when slight changes occur in the parameters of the model they were generated for. The literature lacks a way to provide exhaustive robustness guarantees for CEs under model changes, in that existing methods to improve CEs' robustness are mostly heuristic, and the robustness performances are evaluated empirically using only a limited number of retrained models. To bridge this gap, we propose a novel interval abstraction technique for parametric machine learning models, which allows us to obtain provable robustness guarantees for CEs under a possibly infinite set of plausible model changes Δ. Based on this idea, we formalise a robustness notion for CEs, which we call Δ-robustness, in both binary and multi-class classification settings. We present procedures to verify Δ-robustness based on Mixed Integer Linear Programming, using which we further propose algorithms to generate CEs that are Δ-robust. In an extensive empirical study involving neural networks and logistic regression models, we demonstrate the practical applicability of our approach. We discuss two strategies for determining the appropriate hyperparameters in our method, and we quantitatively benchmark CEs generated by eleven methods, highlighting the effectiveness of our algorithms in finding robust CEs.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"336 ","pages":"Article 104218"},"PeriodicalIF":5.1,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0004370224001541/pdfft?md5=8e8f378cd7774b1862ed4a88c2531907&pid=1-s2.0-S0004370224001541-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142130124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}