Pub Date : 2025-09-08DOI: 10.1016/j.artint.2025.104409
Luigi Bonassi , Giuseppe De Giacomo , Marco Favorito , Francesco Fuggitti , Alfonso Emilio Gerevini , Enrico Scala
We study planning for temporally extended goals expressed in Pure-Past Linear Temporal Logic (ppltl) in the context of deterministic (i.e., classical) and fully observable nondeterministic (FOND) domains. ppltl is the variant of Linear-time Temporal Logic on finite traces (ltlf) that refers to the past rather than the future. Although ppltl is as expressive as ltlf, we show that it is computationally much more effective for planning. In particular, we show that checking the validity of a plan for a ppltl formula is Markovian. This is achieved by introducing a linear number of additional propositional variables that capture the validity of the entire formula in a modular fashion. The solution encoding introduces only a linear number of new fluents proportional to the size of the ppltl goal and does not require any additional spurious action. We implement our solution technique in a system called , which can be used alongside state-of-the-art classical and FOND planners. Our empirical analysis demonstrates the practical effectiveness of in both classical and FOND problems, showing that the resulting planner performs overall better than other planning approaches for ltlf goals.
{"title":"Planning for temporally extended goals in pure-past linear temporal logic","authors":"Luigi Bonassi , Giuseppe De Giacomo , Marco Favorito , Francesco Fuggitti , Alfonso Emilio Gerevini , Enrico Scala","doi":"10.1016/j.artint.2025.104409","DOIUrl":"10.1016/j.artint.2025.104409","url":null,"abstract":"<div><div>We study planning for temporally extended goals expressed in Pure-Past Linear Temporal Logic (<span>ppltl</span>) in the context of deterministic (i.e., classical) and fully observable nondeterministic (FOND) domains. <span>ppltl</span> is the variant of Linear-time Temporal Logic on finite traces (<span>ltl</span><sub><em>f</em></sub>) that refers to the past rather than the future. Although <span>ppltl</span> is as expressive as <span>ltl</span><sub><em>f</em></sub>, we show that it is computationally much more effective for planning. In particular, we show that checking the validity of a plan for a <span>ppltl</span> formula is Markovian. This is achieved by introducing a linear number of additional propositional variables that capture the validity of the entire formula in a modular fashion. The solution encoding introduces only a linear number of new fluents proportional to the size of the <span>ppltl</span> goal and does not require any additional spurious action. We implement our solution technique in a system called <span><math><mi>Plan4Past</mi></math></span>, which can be used alongside state-of-the-art classical and FOND planners. Our empirical analysis demonstrates the practical effectiveness of <span><math><mi>Plan4Past</mi></math></span> in both classical and FOND problems, showing that the resulting planner performs overall better than other planning approaches for <span>ltl</span><sub><em>f</em></sub> goals.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"348 ","pages":"Article 104409"},"PeriodicalIF":4.6,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145044398","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}
Pub Date : 2025-09-02DOI: 10.1016/j.artint.2025.104408
Ryan Carey , Eric Langlois , Chris van Merwijk , Shane Legg , Tom Everitt
We introduce three concepts that describe an agent's incentives: response incentives indicate which variables in the environment, such as sensitive demographic information, affect the decision under the optimal policy. Instrumental control incentives indicate whether an agent's policy is chosen to manipulate part of its environment, such as the preferences or instructions of a user. Impact incentives indicate which variables an agent will affect, intentionally or otherwise. For each concept, we establish sound and complete graphical criteria, and discuss general classes of techniques that may be used to produce incentives for safe and fair agent behaviour. Finally, we outline how these notions may be generalised to multi-decision settings.
This journal paper extends our conference publication “Agent Incentives: A Causal Perspective”: the material on response incentives and instrumental control incentives is updated, while the work on impact incentives and multi-decision settings is entirely new.
我们引入了描述agent激励的三个概念：响应激励表明环境中的哪些变量，如敏感的人口统计信息，会影响最优策略下的决策；工具控制激励指示代理是否选择策略来操纵其环境的一部分，例如用户的偏好或指令。影响激励表明代理人有意或无意地影响哪些变量。对于每个概念，我们建立了健全和完整的图形标准，并讨论了可用于产生安全和公平代理行为激励的一般技术类别。最后，我们概述了如何将这些概念推广到多决策设置。这篇期刊论文扩展了我们的会议出版物“Agent Incentives: A Causal Perspective”：更新了关于响应激励和工具控制激励的材料，而关于影响激励和多决策设置的工作则是全新的。
{"title":"Incentives for responsiveness, instrumental control and impact","authors":"Ryan Carey , Eric Langlois , Chris van Merwijk , Shane Legg , Tom Everitt","doi":"10.1016/j.artint.2025.104408","DOIUrl":"10.1016/j.artint.2025.104408","url":null,"abstract":"<div><div>We introduce three concepts that describe an agent's incentives: response incentives indicate which variables in the environment, such as sensitive demographic information, affect the decision under the optimal policy. Instrumental control incentives indicate whether an agent's policy is chosen to manipulate part of its environment, such as the preferences or instructions of a user. Impact incentives indicate which variables an agent will affect, intentionally or otherwise. For each concept, we establish sound and complete graphical criteria, and discuss general classes of techniques that may be used to produce incentives for safe and fair agent behaviour. Finally, we outline how these notions may be generalised to multi-decision settings.</div><div>This journal paper extends our conference publication “Agent Incentives: A Causal Perspective”: the material on response incentives and instrumental control incentives is updated, while the work on impact incentives and multi-decision settings is entirely new.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"348 ","pages":"Article 104408"},"PeriodicalIF":4.6,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145018406","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}
Pub Date : 2025-09-01DOI: 10.1016/j.artint.2025.104407
Bita Banihashemi , Giuseppe De Giacomo , Yves Lespérance
We develop a general framework for agent abstraction based on the situation calculus and the ConGolog agent programming language. We assume that we have a high-level specification and a low-level specification of the agent, both represented as basic action theories. A refinement mapping specifies how each high-level action is implemented by a low-level ConGolog program and how each high-level fluent can be translated into a low-level formula. We define a notion of sound abstraction between such action theories in terms of the existence of a suitable bisimulation between their respective models. Sound abstractions have many useful properties that ensure that we can reason about the agent's actions (e.g., executability, projection, and planning) at the abstract level, and refine and concretely execute them at the low level. We also characterize the notion of complete abstraction where all actions (including exogenous ones) that the high level thinks can happen can in fact occur at the low level. To facilitate verifying that one has a sound/complete abstraction relative to a mapping, we provide a set of necessary and sufficient conditions. Finally, we identify a set of basic action theory constraints that ensure that for any low-level action sequence, there is a unique high-level action sequence that it refines. This allows us to track/monitor what the low-level agent is doing and describe it in abstract terms (i.e., provide high-level explanations, for instance, to a client or manager).
{"title":"Abstracting situation calculus action theories","authors":"Bita Banihashemi , Giuseppe De Giacomo , Yves Lespérance","doi":"10.1016/j.artint.2025.104407","DOIUrl":"10.1016/j.artint.2025.104407","url":null,"abstract":"<div><div>We develop a general framework for <em>agent abstraction</em> based on the situation calculus and the <span>ConGolog</span> agent programming language. We assume that we have a high-level specification and a low-level specification of the agent, both represented as basic action theories. A <em>refinement mapping</em> specifies how each high-level action is implemented by a low-level <span>ConGolog</span> program and how each high-level fluent can be translated into a low-level formula. We define a notion of <em>sound abstraction</em> between such action theories in terms of the existence of a suitable bisimulation between their respective models. Sound abstractions have many useful properties that ensure that we can reason about the agent's actions (e.g., executability, projection, and planning) at the abstract level, and refine and concretely execute them at the low level. We also characterize the notion of <em>complete abstraction</em> where all actions (including exogenous ones) that the high level thinks can happen can in fact occur at the low level. To facilitate verifying that one has a sound/complete abstraction relative to a mapping, we provide a set of necessary and sufficient conditions. Finally, we identify a set of basic action theory constraints that ensure that for any low-level action sequence, there is a unique high-level action sequence that it refines. This allows us to track/monitor what the low-level agent is doing and describe it in abstract terms (i.e., provide high-level explanations, for instance, to a client or manager).</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"348 ","pages":"Article 104407"},"PeriodicalIF":4.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144988707","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}
Pub Date : 2025-08-28DOI: 10.1016/j.artint.2025.104400
Tanya Ignatenko , Kirill Kondrashov , Marco Cox , Bert de Vries
User preference learning is generally a hard problem. Individual preferences are typically unknown even to users themselves, while the space of choices is infinite. Here we study user preference learning from information-theoretic perspective. We model preference learning as a system with two interacting sub-systems, one representing a user with his/her preferences and another one representing an agent that has to learn these preferences. The user with his/her behavior is modeled by a parametric preference function. To efficiently learn the preferences and reduce search space quickly, we propose the agent that interacts with the user to collect the most informative data for learning. The agent presents two proposals to the user for evaluation, and the user rates them based on his/her preference function. We show that the optimum agent strategy for data collection and preference learning is a result of maximin optimization of the normalized weighted Kullback-Leibler (KL) divergence between true and agent-assigned predictive user response distributions. The resulting value of the KL-divergence, which we also call of a remaining system uncertainty (RSU), provides an efficient performance metric in the absence of the ground truth. This metric characterizes how well the agent can predict user and, thus, the quality of the underlying learned user (preference) model. Our proposed agent comprises sequential mechanisms for user model inference and proposal generation. To infer the user model (preference function), Bayesian approximate inference is used in the agent. The data collection strategy is to generate proposals, responses to which help resolving uncertainty associated with prediction of the user responses the most. The efficiency of our approach is validated by numerical simulations. Also a real-life example of preference learning application is provided.
{"title":"On preference learning based on sequential Bayesian optimization with pairwise comparison","authors":"Tanya Ignatenko , Kirill Kondrashov , Marco Cox , Bert de Vries","doi":"10.1016/j.artint.2025.104400","DOIUrl":"10.1016/j.artint.2025.104400","url":null,"abstract":"<div><div>User preference learning is generally a hard problem. Individual preferences are typically unknown even to users themselves, while the space of choices is infinite. Here we study user preference learning from information-theoretic perspective. We model preference learning as a system with two interacting sub-systems, one representing a user with his/her preferences and another one representing an agent that has to learn these preferences. The user with his/her behavior is modeled by a parametric preference function. To efficiently learn the preferences and reduce search space quickly, we propose the agent that interacts with the user to collect the most informative data for learning. The agent presents two proposals to the user for evaluation, and the user rates them based on his/her preference function. We show that the optimum agent strategy for data collection and preference learning is a result of maximin optimization of the normalized weighted Kullback-Leibler (KL) divergence between true and agent-assigned predictive user response distributions. The resulting value of the KL-divergence, which we also call of a remaining system uncertainty (RSU), provides an efficient performance metric in the absence of the ground truth. This metric characterizes how well the agent can predict user and, thus, the quality of the underlying learned user (preference) model. Our proposed agent comprises sequential mechanisms for user model inference and proposal generation. To infer the user model (preference function), Bayesian approximate inference is used in the agent. The data collection strategy is to generate proposals, responses to which help resolving uncertainty associated with prediction of the user responses the most. The efficiency of our approach is validated by numerical simulations. Also a real-life example of preference learning application is provided.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"348 ","pages":"Article 104400"},"PeriodicalIF":4.6,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145018407","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}
We study the fair division of indivisible items with subsidies among n agents, where the absolute marginal valuation of each item is at most one. Under monotone nondecreasing valuations (where each item is a good), Brustle et al. [9] demonstrated that a maximum subsidy of and a total subsidy of are sufficient to guarantee the existence of an envy-freeable allocation. In this paper, we improve upon these bounds, even in a wider model. Namely, we show that, given an EF1 allocation, we can compute in polynomial time an envy-free allocation with a subsidy of at most per agent and a total subsidy of at most . Moreover, when the valuations are monotone nondecreasing, we provide a polynomial-time algorithm that computes an envy-free allocation with a subsidy of at most per agent and a total subsidy of at most .
{"title":"Towards optimal subsidy bounds for envy-freeable allocations","authors":"Yasushi Kawase , Kazuhisa Makino , Hanna Sumita , Akihisa Tamura , Makoto Yokoo","doi":"10.1016/j.artint.2025.104406","DOIUrl":"10.1016/j.artint.2025.104406","url":null,"abstract":"<div><div>We study the fair division of indivisible items with subsidies among <em>n</em> agents, where the absolute marginal valuation of each item is at most one. Under monotone nondecreasing valuations (where each item is a good), Brustle et al. <span><span>[9]</span></span> demonstrated that a maximum subsidy of <span><math><mn>2</mn><mo>(</mo><mi>n</mi><mo>−</mo><mn>1</mn><mo>)</mo></math></span> and a total subsidy of <span><math><mn>2</mn><msup><mrow><mo>(</mo><mi>n</mi><mo>−</mo><mn>1</mn><mo>)</mo></mrow><mrow><mn>2</mn></mrow></msup></math></span> are sufficient to guarantee the existence of an envy-freeable allocation. In this paper, we improve upon these bounds, even in a wider model. Namely, we show that, given an EF1 allocation, we can compute in polynomial time an envy-free allocation with a subsidy of at most <span><math><mi>n</mi><mo>−</mo><mn>1</mn></math></span> per agent and a total subsidy of at most <span><math><mi>n</mi><mo>(</mo><mi>n</mi><mo>−</mo><mn>1</mn><mo>)</mo><mo>/</mo><mn>2</mn></math></span>. Moreover, when the valuations are monotone nondecreasing, we provide a polynomial-time algorithm that computes an envy-free allocation with a subsidy of at most <span><math><mi>n</mi><mo>−</mo><mn>1.5</mn></math></span> per agent and a total subsidy of at most <span><math><mo>(</mo><msup><mrow><mi>n</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>−</mo><mi>n</mi><mo>−</mo><mn>1</mn><mo>)</mo><mo>/</mo><mn>2</mn></math></span>.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"348 ","pages":"Article 104406"},"PeriodicalIF":4.6,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144912200","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}
Pub Date : 2025-08-27DOI: 10.1016/j.artint.2025.104405
Peng Lin , Shaowei Cai , Mengchuan Zou , Jinkun Lin
Mixed Integer Programming (MIP) is a fundamental model in operations research with broad industrial applications. Local search is a powerful methodology for solving complex optimization problems; however, the development of local search algorithms for MIP still needs exploration. In this work, we propose Local-MIP, an efficient local search algorithm tailored for MIP that integrates novel operators and employs a two-mode architecture to adaptively apply operators based on the current solution's feasibility. For the feasible mode, we propose the lift move operator and a corresponding lift process to improve the objective value while maintaining feasibility. For the infeasible mode, we propose the breakthrough move and mixed tight move operators to respectively optimize the objective function and satisfy constraints. To apply operators intelligently, we develop a dynamic weighting scheme that balances the priorities of the objective function and constraints. Furthermore, we propose a two-level scoring function structure that hierarchically selects operations, guiding the search toward high-quality feasible solutions. Experiments are conducted on public benchmarks to compare Local-MIP with state-of-the-art MIP solvers in finding high-quality solutions. The results show that Local-MIP significantly outperforms CPLEX, HiGHS, SCIP, and Feasibility Jump while remaining competitive with the commercial solver Gurobi on challenging problems within short time limits. Moreover, Local-MIP establishes 10 new records on MIPLIB open instances.
{"title":"Local-MIP: Efficient local search for mixed integer programming","authors":"Peng Lin , Shaowei Cai , Mengchuan Zou , Jinkun Lin","doi":"10.1016/j.artint.2025.104405","DOIUrl":"10.1016/j.artint.2025.104405","url":null,"abstract":"<div><div>Mixed Integer Programming (MIP) is a fundamental model in operations research with broad industrial applications. Local search is a powerful methodology for solving complex optimization problems; however, the development of local search algorithms for MIP still needs exploration. In this work, we propose <em>Local-MIP</em>, an efficient local search algorithm tailored for MIP that integrates novel operators and employs a two-mode architecture to adaptively apply operators based on the current solution's feasibility. For the feasible mode, we propose the lift move operator and a corresponding lift process to improve the objective value while maintaining feasibility. For the infeasible mode, we propose the breakthrough move and mixed tight move operators to respectively optimize the objective function and satisfy constraints. To apply operators intelligently, we develop a dynamic weighting scheme that balances the priorities of the objective function and constraints. Furthermore, we propose a two-level scoring function structure that hierarchically selects operations, guiding the search toward high-quality feasible solutions. Experiments are conducted on public benchmarks to compare <em>Local-MIP</em> with state-of-the-art MIP solvers in finding high-quality solutions. The results show that <em>Local-MIP</em> significantly outperforms <em>CPLEX</em>, <em>HiGHS</em>, <em>SCIP</em>, and <em>Feasibility Jump</em> while remaining competitive with the commercial solver <em>Gurobi</em> on challenging problems within short time limits. Moreover, <em>Local-MIP</em> establishes 10 new records on MIPLIB open instances.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"348 ","pages":"Article 104405"},"PeriodicalIF":4.6,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144922750","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}
Pub Date : 2025-08-20DOI: 10.1016/j.artint.2025.104403
Alexander Dean, Eduardo Alonso, Esther Mondragón
Learning efficient representations allows robust processing of data, data that can then be generalised across different tasks and domains, and it is thus paramount in various areas of Artificial Intelligence, including computer vision, natural language processing and reinforcement learning, among others. Within the context of reinforcement learning, we propose in this paper a mathematical framework to learn representations by extracting the algebra of the transformations of worlds from the perspective of an agent. As a starting point, we use our framework to reproduce representations from the symmetry-based disentangled representation learning (SBDRL) formalism proposed by [1] and prove that, although useful, they are restricted to transformations that respond to the properties of algebraic groups. We then generalise two important results of SBDRL –the equivariance condition and the disentangling definition– from only working with group-based symmetry representations to working with representations capturing the transformation properties of worlds for any algebra, using examples common in reinforcement learning and generated by an algorithm that computes their corresponding Cayley tables. Finally, we combine our generalised equivariance condition and our generalised disentangling definition to show that disentangled sub-algebras can each have their own individual equivariance conditions, which can be treated independently, using category theory. In so doing, our framework offers a rich formal tool to represent different types of symmetry transformations in reinforcement learning, extending the scope of previous proposals and providing Artificial Intelligence developers with a sound foundation to implement efficient applications.
{"title":"Algebras of actions in an agent's representations of the world","authors":"Alexander Dean, Eduardo Alonso, Esther Mondragón","doi":"10.1016/j.artint.2025.104403","DOIUrl":"10.1016/j.artint.2025.104403","url":null,"abstract":"<div><div>Learning efficient representations allows robust processing of data, data that can then be generalised across different tasks and domains, and it is thus paramount in various areas of Artificial Intelligence, including computer vision, natural language processing and reinforcement learning, among others. Within the context of reinforcement learning, we propose in this paper a mathematical framework to learn representations by extracting the algebra of the transformations of worlds from the perspective of an agent. As a starting point, we use our framework to reproduce representations from the symmetry-based disentangled representation learning (SBDRL) formalism proposed by <span><span>[1]</span></span> and prove that, although useful, they are restricted to transformations that respond to the properties of algebraic groups. We then generalise two important results of SBDRL –the equivariance condition and the disentangling definition– from only working with group-based symmetry representations to working with representations capturing the transformation properties of worlds for any algebra, using examples common in reinforcement learning and generated by an algorithm that computes their corresponding Cayley tables. Finally, we combine our generalised equivariance condition and our generalised disentangling definition to show that disentangled sub-algebras can each have their own individual equivariance conditions, which can be treated independently, using category theory. In so doing, our framework offers a rich formal tool to represent different types of symmetry transformations in reinforcement learning, extending the scope of previous proposals and providing Artificial Intelligence developers with a sound foundation to implement efficient applications.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"348 ","pages":"Article 104403"},"PeriodicalIF":4.6,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144886771","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}
Pub Date : 2025-08-20DOI: 10.1016/j.artint.2025.104399
Patrick Rodler , Birgit Hofer , Dietmar Jannach , Iulia Nica , Franz Wotawa
Model-based diagnosis is a generally applicable, principled approach to the systematic debugging of a wide range of system types such as circuits, knowledge bases, physical devices, or software. Based on a formal description of the system, it enables precise and deterministic reasoning about potential faults responsible for observed misbehavior. In software, such a formal system description can often even be extracted from the buggy program fully automatically. As logical reasoning is central to diagnosis, the performance of model-based debuggers is largely influenced by reasoning efficiency, which in turn depends on the complexity and expressivity of the system description. Since highly detailed models capturing exact semantics often exceed the capabilities of current reasoning tools, researchers have proposed more abstract representations.
In this work, we thoroughly analyze system modeling techniques with a focus on fault localization in spreadsheets—one of the most widely used end-user programming paradigms. Specifically, we present three constraint model types characterizing spreadsheets at different abstraction levels, show how to extract them automatically from faulty spreadsheets, and provide theoretical and empirical investigations of the impact of abstraction on both diagnostic output and computational performance. Our main conclusions are that (i) for the model types, there is a trade-off between the conciseness of generated fault candidates and computation time, (ii) the exact model is often impractical, and (iii) a new model based on qualitative reasoning yields the same solutions as the exact one in up to more than half the cases while being orders of magnitude faster.
Due to their ability to restrict the solution space in a sound way, the explored model-based techniques, rather than being used as standalone approaches, are expected to realize their full potential in combination with iterative sequential diagnosis or indeterministic but more performant statistical debugging methods.
{"title":"Choosing abstraction levels for model-based software debugging: A theoretical and empirical analysis for spreadsheet programs","authors":"Patrick Rodler , Birgit Hofer , Dietmar Jannach , Iulia Nica , Franz Wotawa","doi":"10.1016/j.artint.2025.104399","DOIUrl":"10.1016/j.artint.2025.104399","url":null,"abstract":"<div><div>Model-based diagnosis is a generally applicable, principled approach to the systematic debugging of a wide range of system types such as circuits, knowledge bases, physical devices, or software. Based on a formal description of the system, it enables precise and deterministic reasoning about potential faults responsible for observed misbehavior. In software, such a formal system description can often even be extracted from the buggy program fully automatically. As logical reasoning is central to diagnosis, the performance of model-based debuggers is largely influenced by reasoning efficiency, which in turn depends on the complexity and expressivity of the system description. Since highly detailed models capturing exact semantics often exceed the capabilities of current reasoning tools, researchers have proposed more abstract representations.</div><div>In this work, we thoroughly analyze system modeling techniques with a focus on fault localization in spreadsheets—one of the most widely used end-user programming paradigms. Specifically, we present three constraint model types characterizing spreadsheets at different abstraction levels, show how to extract them automatically from faulty spreadsheets, and provide theoretical and empirical investigations of the impact of abstraction on both diagnostic output and computational performance. Our main conclusions are that <em>(i)</em> for the model types, there is a trade-off between the conciseness of generated fault candidates and computation time, <em>(ii)</em> the exact model is often impractical, and <em>(iii)</em> a new model based on qualitative reasoning yields the same solutions as the exact one in up to more than half the cases while being orders of magnitude faster.</div><div>Due to their ability to restrict the solution space in a sound way, the explored model-based techniques, rather than being used as standalone approaches, are expected to realize their full potential in combination with iterative sequential diagnosis or indeterministic but more performant statistical debugging methods.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"348 ","pages":"Article 104399"},"PeriodicalIF":4.6,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144898569","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}
Pub Date : 2025-08-12DOI: 10.1016/j.artint.2025.104402
Piero Bonatti , Gianluca Cima , Domenico Lembo , Francesco Magliocca , Lorenzo Marconi , Riccardo Rosati , Luigi Sauro , Domenico Fabio Savo
Controlled Query Evaluation (CQE) is a methodology designed to maintain confidentiality by either rejecting specific queries or adjusting responses to safeguard sensitive information. In this investigation, our focus centers on CQE within Description Logic ontologies, aiming to ensure that queries are answered truthfully as long as possible before resorting to deceptive responses, a cooperativity property which is called the “longest honeymoon”. Our work introduces new semantics for CQE, denoted as MC-CQE, which enjoys the longest honeymoon property and outperforms previous methodologies in terms of cooperativity.
We study the complexity of query answering in this new framework for ontologies expressed in the Description Logic . Specifically, we establish data complexity results under different maximally cooperative semantics and for different classes of queries. Our results identify both tractable and intractable cases. In particular, we show that the evaluation of Boolean unions of conjunctive queries is the same under all the above semantics and its data complexity is in . This result makes query answering amenable to SQL query rewriting. However, this favorable property does not extend to open queries, even with a restricted query language limited to conjunctions of atoms. While, in general, answering open queries in the MC-CQE framework is intractable, we identify a sub-family of semantics under which answering full conjunctive queries is tractable.
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Pub Date : 2025-08-08DOI: 10.1016/j.artint.2025.104401
Yingji Li , Mengnan Du , Rui Song , Mu Liu , Ying Wang
With the rapid development of Pre-trained Language Models (PLMs) and their widespread deployment in various real-world applications, social biases of PLMs have attracted increasing attention, especially the fairness of downstream tasks, which potentially affects the development and stability of society. Among existing debiasing methods, intrinsic debiasing methods are not necessarily effective when applied to downstream tasks, and the downstream fine-tuning process may introduce new biases or catastrophic forgetting. Most extrinsic debiasing methods rely on sensitive attribute words as prior knowledge to supervise debiasing training. However, it is difficult to collect sensitive attribute information of real data due to privacy and regulation. Moreover, limited sensitive attribute words may lead to inadequate debiasing training. To this end, this paper proposes a debiasing method to learn fair representation for PLMs via BiAsed TEacher-guided Disentanglement (called BATED). Specific to downstream tasks, BATED performs debiasing training under the guidance of a biased teacher model rather than relying on sensitive attribute information of the training data. First, we leverage causal contrastive learning to train a task-agnostic general biased teacher model. We then employ Variational Auto-Encoder (VAE) to disentangle the PLM-encoded representation into the fair representation and the biased representation. The Biased representation is further decoupled via biased teacher-guided disentanglement, while the fair representation learn downstream tasks. Therefore, BATED guarantees the performance of downstream tasks while improving the fairness. Experimental results on seven PLMs testing three downstream tasks demonstrate that BATED outperforms the state-of-the-art overall in terms of fairness and performance on downstream tasks.
随着预训练语言模型（Pre-trained Language Models, PLMs）的快速发展和在各种现实应用中的广泛应用，PLMs的社会偏见越来越受到人们的关注，尤其是下游任务的公平性问题，它可能会影响社会的发展和稳定。在现有的去偏方法中，内在去偏方法在应用于下游任务时不一定有效，下游微调过程可能会引入新的偏差或灾难性遗忘。大多数外在去偏方法依赖敏感属性词作为先验知识来监督去偏训练。然而，由于隐私和监管的原因，难以收集到真实数据的敏感属性信息。此外，有限的敏感属性词可能导致去偏训练不足。为此，本文提出了一种通过有偏见的教师引导的解纠缠（BATED）来学习plm公平表示的去偏见方法。针对下游任务，BATED在偏向教师模型的指导下进行去偏向训练，而不是依赖于训练数据的敏感属性信息。首先，我们利用因果对比学习来训练一个任务不可知论的一般偏见教师模型。然后，我们使用变分自编码器（VAE）将plm编码表示分解为公平表示和偏见表示。有偏见的表示通过有偏见的教师引导的解纠缠进一步解耦，而公平表示学习下游任务。因此，BATED在保证下游任务性能的同时，提高了公平性。在七个plm测试三个下游任务的实验结果表明，BATED在下游任务的公平性和性能方面总体上优于最先进的技术。
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