We focus on the problem of inducing logic programs that explain models learned by the support vector machine (SVM) algorithm. The top-down sequential covering inductive logicprogramming (ILP) algorithms (e.g., FOIL) ...
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We focus on the problem of inducing logic programs that explain models learned by the support vector machine (SVM) algorithm. The top-down sequential covering inductive logicprogramming (ILP) algorithms (e.g., FOIL) apply hill-climbing search using heuristics from information theory. A major issue with this class of algorithms is getting stuck in local optima. In our new approach, however, the data-dependent hill-climbing search is replaced with a model-dependent search where a globally optimal SVM model is trained first, then the algorithm looks into support vectors as the most influential data points in the model, and induces a clause that would cover the support vector and points that are most similar to that support vector. Instead of defining a fixed hypothesis search space, our algorithm makes use of SHAP, an example-specific interpreter in explainable AI, to determine a relevant set of features. This approach yields an algorithm that captures the SVM model's underlying logic and outperforms other ILP algorithms in terms of the number of induced clauses and classification evaluation metrics.
This paper continues the line of research aimed at investigating the relationship between logic programs and first-order theories. We extend the definition of program completion to programs with input and output in a ...
This paper continues the line of research aimed at investigating the relationship between logic programs and first-order theories. We extend the definition of program completion to programs with input and output in a subset of the input language of the ASP grounder gringo, study the relationship between stable models and completion in this context, and describe preliminary experiments with the use of two software tools, anthem and vampire, for verifying the correctness of programs with input and output. Proofs of theorems are based on a lemma that relates the semantics of programs studied in this paper to stable models of first-order formulas.
Search-optimization problems are plentiful in scientific and engineering domains. Artificial intelligence has long contributed to the development of search algorithms and declarative programming languages geared towar...
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Recursive definitions of predicates are usually interpreted either inductively or coinductively. Recently, a more powerful approach has been proposed, calledflexible coinduction, to express a variety of intermediate i...
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Recursive definitions of predicates are usually interpreted either inductively or coinductively. Recently, a more powerful approach has been proposed, calledflexible coinduction, to express a variety of intermediate interpretations, necessary in some cases to get the correct meaning. We provide a detailed formal account of an extension of logicprogramming supporting flexible coinduction. Syntactically, programs are enriched bycoclauses, clauses with a special meaning used to tune the interpretation of predicates. As usual, the declarative semantics can be expressed as a fixed point which, however, is not necessarily the least, nor the greatest one, but is determined by the coclauses. Correspondingly, the operational semantics is a combination of standard SLD resolution and coSLD resolution. We prove that the operational semantics is sound and complete with respect to declarative semantics restricted to finite comodels.
The airline industry has evolved significantly in recent years, and the emphasis on minimizing the cost and risk of operational disruptions has been shifted, in part, to satisfaction and fairness in the assignment of ...
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The airline industry has evolved significantly in recent years, and the emphasis on minimizing the cost and risk of operational disruptions has been shifted, in part, to satisfaction and fairness in the assignment of crew members. The crew rostering is the phase of flight schedules in which crew members are assigned to rosters based on their preferences and fairness in terms of balanced flight times between crew members. The satisfaction of the crew members depends on how much their preferences are met in the rosters. In this study, the crew rostering problem with cabin members' preferences is formulated as a mixed-integer goal programming model with three objective functions: total satisfaction score, deviation from average satisfaction score, and deviation from average flight hours. The importance of these objectives is imprecise from the flight management system perspective, so this study proposes a fuzzy goal programming model to solve the crew rostering problem with the preferences. The paper is organized based on a case study gathered from an airline in Kuwait. The results showed that incorporating pilot preferences leads to better rosters in terms of pilot satisfaction. However, increasing the number of pilots in consideration or the flight evaluation percentage does not lead to better rosters. The case study also sheds light on the links between the crew roster and sustainable operations.
Several formal systems, such as resolution and minimal model semantics, provide a framework for logicprogramming. In this paper, we will survey the use of structural proof theory as an alternative foundation. Researc...
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The decoupling between the representation of a certain problem, i.e., its knowledge model, and the reasoning side is one of main strong points of model-based Artificial Intelligence (AI). This allows, e.g. to focus on...
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We describe eclingo, a solver for epistemic logic programs under Gelfond 1991 semantics built upon the Answer Set programming system clingo. The input language of eclingo uses the syntax extension capabilities of clin...
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We describe eclingo, a solver for epistemic logic programs under Gelfond 1991 semantics built upon the Answer Set programming system clingo. The input language of eclingo uses the syntax extension capabilities of clingo to define subjective literals that, as usual in epistemic logic programs, allow for checking the truth of a regular literal in all or in some of the answer sets of a program. The eclingo solving process follows a guess and check strategy. It first generates potential truth values for subjective literals and, in a second step, it checks the obtained result with respect to the cautious and brave consequences of the program. This process is implemented using the multi-shot functionalities of clingo. We have also implemented some optimisations, aiming at reducing the search space and, therefore, increasing eclingo 's efficiency in some scenarios. Finally, we compare the efficiency of eclingo with two state-of-the-art solvers for epistemic logic programs on a pair of benchmark scenarios and show that eclingo generally outperforms their obtained results.
We present OntoScene, a framework aimed at understanding the semantics of visual scenes starting from the semantics of their elements and the spatial relations holding between them. OntoScene exploits ontologies for r...
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We present OntoScene, a framework aimed at understanding the semantics of visual scenes starting from the semantics of their elements and the spatial relations holding between them. OntoScene exploits ontologies for representing knowledge and Prolog for specifying the interpretation rules that domain experts may adopt, and for implementing the SceneInterpreter engine. Ontologies allow the designer to formalize the domain in a reusable way and make the system modular and interoperable with existing multiagent systems, while Prolog provides a solid basis to define complex rules of interpretation in a way that can be affordable even for people with no background in Computational logics. The domain selected for experimenting OntoScene is that of prehistoric rock art, which provides us with a fascinating and challenging testbed.
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