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INTELLIGENZA ARTIFICIALE 2011年第1期5卷 133-138页

作者： Greco, Sergio Leone, Nicola Palopoli, Luigi Rullo, Pasquale Sacca, Domenico Univ Calabria DEIS Reude Italy Univ Calabria Dip Matemat Reude Italy

In this paper, we describe the research lines in logic programming, carried out in Cosenza over a period of more than twenty years, which have recently produced promising industrial exploitation follow-ups. The research lines have changed over the time but they have kept the initial interest on combining logic programming with databases techniques, that has been continuously renewed to cope with new challenges, in our attempt to use theory to solve practical problems.

关键词： Logic programming DATALOG bottom-up execution stable models disjunctive logic programming answer set programming ontology

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Quantifying means-end reasoning skills in simulation-based training: a logic-based approach

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SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL 2022年第10期98卷 933-957页

作者： Stolpe, Audun Hannay, Jo Erskine Norwegian Comp Ctr Dept Appl Res Informat Technol POB 114 NO-0314 Oslo Norway Simula Metropolitan Ctr Digital Engn Publ Sect Ctr Effect Digitalizat Oslo Norway

We develop a logic-based approach for designing simulation-based training scenarios. Our methodology embodies a concise definition of the scenario concept and integrates the notions of training goals, acceptable versus unacceptable actions and performance scoring. The approach applies classical artificial intelligence (AI) planning to extract coherent plays from a causal description of the training domain. The domain- and task-specific parts are defined in a high-level action description language AL. Generic causal and temporal logic is added when the causal theory is compiled into the underlying answer set programming (ASP) language. The ASP representation is used to derive a scoring function that reflects the quality of a play or training session, based on a distinction of states and actions into green (acceptable) and red (unacceptable) ones. To that end, we add to the casual theory a set of norms that specify an initial assignment of colors. The ASP engine uses these norms as axioms and propagates colors by consulting the causal theory. We prove that any set of such norms constitutes a conservative extension of the underlying causal theory. With this work, we hope to lay the foundation for the development of design and analysis tools for exercise managers. We envision a software system that lets an exercise manager view all plays of a tentative scenario design, with expediency information and scores for each possible play. Our approach is applicable to any domain in which means-ends reasoning is pertinent. We illustrate the approach in the domain of crisis response and management.

关键词： Simulation-based training means-end reasoning automated scoring answer set programming deontic logic

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A polynomial reduction of forks into logic programs

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ARTIFICIAL INTELLIGENCE 2022年 308卷 103712-103712页

作者： Aguado, Felicidad Cabalar, Pedro Fandinno, Jorge Pearce, David Perez, Gilberto Vidal, Concepcion Univ A Coruna La Coruna Spain CITIC Res Ctr La Coruna Spain Univ Nebraska Omaha NE 68182 USA Univ Politecn Madrid Madrid Spain

In this research note we present additional results for an earlier published paper [1]. There, we studied the problem of projective strong equivalence (PSE) of logic programs, that is, checking whether two logic programs (or propositional formulas) have the same behaviour (under the stable model semantics) regardless of a common context and ignoring the effect of local auxiliary atoms. PSE is related to another problem called strongly persistent forgetting that consists in keeping a program's behaviour after removing its auxiliary atoms, something that is known to be not always possible in answer set programming. In [1], we introduced a new connective '|' called fork and proved that, in this extended language, it is always possible to forget auxiliary atoms, but at the price of obtaining a result containing forks. We also proved that forks can be translated back to logic programs introducing new hidden auxiliary atoms, but this translation was exponential in the worst case. In this note we provide a new polynomial translation of arbitrary forks into regular programs that allows us to prove that brave and cautious reasoning with forks has the same complexity as that of ordinary (disjunctive) logic programs and paves the way for an efficient implementation of forks. To this aim, we rely on a pair of new PSE invariance properties. (C) 2022 The Author(s). Published by Elsevier B.V.

关键词： answer set programming Non-monotonic reasoning Equilibrium logic Denotational semantics Forgetting Strong equivalence

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Forming We-intentions under breakdown situations in human-robot interactions

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023年 242卷 107817-107817页

作者： Guerrero, Esteban Tewari, Maitreyee Kalmi, Panu Lindgren, Helena Univ Vaasa Vaasa Finland Sch Technol & Innovat Informat Syst Sci Vaasa Finland Sch Accounting & Finance Econ Helsinki Finland Umea Univ Umea Sweden Dept Comp Sci Umea Sweden

Background and Objective: When agents (e.g. a person and a social robot) perform a joint activity to achieve a joint goal, they require sharing a relevant group intention, which has been defined as a We-intention. In forming We intentions, breakdown situations due to conflicts between internal and "external" intentions are unavoidable, particularly in healthcare scenarios. To study such We-intention formation and "reparation" of conflicts, this paper has a two-fold objective: introduce a general computational mechanism allowing We-intention formation and reparation in interactions between a social robot and a person;and exemplify how the formal framework can be applied to facilitate interaction between a person and a social robot for healthcare scenarios. Method: The formal computational framework for managing We-intentions was defined in terms of answer set programming and a Belief-Desire-Intention control loop. We exemplify the formal framework based on earlier theory-based user studies consisting of human-robot dialogue scenarios conducted in a Wizard of Oz setup, video recorded and evaluated with 20 participants. Data was collected through semi-structured interviews, which were analyzed qualitatively using thematic analysis. N=20 participants (women n=12, men=8, age range 23-72) were part of the study. Two age groups were established for the analysis: younger participants (ages 23-40) and older participants (ages 41-72). Results: We proved four theoretical propositions, which are well-desired characteristics of any rational social robot. In our study, most participants suggested that people were the cause of breakdown situations. Over half of the young participants perceived the social robot's avoidant behavior in the scenarios. Conclusions: This work covered in depth the challenge of aligning the intentions of two agents (for example, in a person-robot interaction) when they try to achieve a joint goal. Our framework provides a novel formalization of the We-int

关键词： We-intentions Breakdown situations Conflict of intentions Repairing conflicts Human-robot interaction answer set programming Logic programming Shared intentions Social robots Healthcare scenarios

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answers set programs for non-transferable utility games: Expressiveness, complexity and applications

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ARTIFICIAL INTELLIGENCE 2022年 302卷 103606-103606页

作者： Amendola, Giovanni Greco, Gianluigi Veltri, Pierfrancesco Univ Calabria Commenda Di Rende Italy

Coalitional games are mathematical models suited to study payoff distribution problems in cooperative scenarios. In abstract terms, a coalitional game can be specified by explicitly listing all possible-in fact, exponentially many-coalitions with their associated distributions. This naive representation, however, quickly becomes infeasible over games involving many agents, thereby calling for suitable compact representations, that is, encoding mechanisms that (on some specific classes of games of interest) take an amount of space that grows polynomially with the number of agents. To date, a plethora of compact encodings have been already introduced and analyzed from the algorithm and computational viewpoints. Despite their specific technical differences, these encodings typically share the assumption that the utility associated with a coalition can be freely transferred among agents. Indeed, designing encoding mechanisms for the non-transferable utility (NTU) setting is a research issue that has been largely unexplored so far. The paper addresses this issue by proposing a compact encoding for representing and reasoning about the outcomes of NTU coalitional games founding on answer set programming. By exploiting the expressiveness of this well-known knowledge representation formalism, it is shown that the proposed representation can succinctly encode several games of interest within a wide range of application domains. Computational issues arising in the setting have been studied too, by addressing questions related to certain payoff distributions enjoying desirable stability properties. Eventually, a prototype system supporting the proposed framework has been implemented by leveraging a state-of-the-art answer set engine, and results of a thorough experimental activity conducted on top of it have been discussed. (C) 2021 Elsevier B.V. All rights reserved.

关键词： answer set programming Game theory Coalitional games NTU games Computational complexity

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Reasoning for sensor data interpretation: An application to air quality monitoring

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JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS 2015年第4期7卷 579-597页

作者： Alirezaie, Marjan Loutfi, Amy Univ Orebro Dept Sci & Technol Ctr Appl Autonomous Sensor Syst AASS SE-70182 Orebro Sweden

In this paper we introduce a representation and reasoning model for the interpretation of time-series signals of a gas sensor situated in a sensor network. The interpretation process includes inferring high level explanations for changes detected over the gas signals. Inspired from the Semantic Sensor Network (SSN), the ontology used in this work provides an adaptive way of modelling the domain-related knowledge. Furthermore, exploiting (Incremental) answer set programming (ASP) enables a declarative and automatic way of rule definition. Converting the ontology concepts and relations into ASP logic programs, the interpretation process defines a logic program whose answer sets are considered as eventual explanations for the detected changes in the gas sensor signals. The proposed approach is tested in a kitchen environment which contains several objects monitored by different sensors. The contextual information provided by the sensor network together with high level domain knowledge are used to infer explanations for changes in the ambient air detected by the gas sensors.

关键词： Semantic sensor ontology non-monotonic reasoning answer set programming knowledge representation

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ASP-based method for the enumeration of attractors in non-deterministic synchronous and asynchronous multi-valued networks

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ALGORITHMS FOR MOLECULAR BIOLOGY 2017年第1期12卷 20-20页

作者： Ben Abdallah, Emna Folschette, Maxime Roux, Olivier Magnin, Morgan Ecole Cent Nantes CNRS LS2N UMR 6004 1 Rue Noe F-44321 Nantes France Univ Nantes CNRS LS2N UMR 6004 1 Rue Noe F-44321 Nantes France Univ Nice Sophia Antipolis CNRS UMR 7271 I3S 2000 Route Lucioles F-06900 Nice France Natl Inst Informat Chiyoda Ku 2-1-2 Hitotsubashi Tokyo 1018430 Japan

Background: This paper addresses the problem of finding attractors in biological regulatory networks. We focus here on non-deterministic synchronous and asynchronous multi-valued networks, modeled using automata networks (AN). AN is a general and well-suited formalism to study complex interactions between different components (genes, proteins, ...). An attractor is a minimal trap domain, that is, a part of the state-transition graph that cannot be escaped. Such structures are terminal components of the dynamics and take the form of steady states (singleton) or complex compositions of cycles (non-singleton). Studying the effect of a disease or a mutation on an organism requires finding the attractors in the model to understand the long-term behaviors. Results: We present a computational logical method based on answer set programming (ASP) to identify all attractors. Performed without any network reduction, the method can be applied on any dynamical semantics. In this paper, we present the two most widespread non-deterministic semantics: the asynchronous and the synchronous updating modes. The logical approach goes through a complete enumeration of the states of the network in order to find the attractors without the necessity to construct the whole state-transition graph. We realize extensive computational experiments which show good performance and fit the expected theoretical results in the literature. Conclusion: The originality of our approach lies on the exhaustive enumeration of all possible (sets of) states verifying the properties of an attractor thanks to the use of ASP. Our method is applied to non-deterministic semantics in two different schemes (asynchronous and synchronous). The merits of our methods are illustrated by applying them to biological examples of various sizes and comparing the results with some existing approaches. It turns out that our approach succeeds to exhaustively enumerate on a desktop computer, in a large model (100 components), all

关键词： Biological regulatory network Multiple-valued networks Attractors Steady states Cycles answer set programming

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Constraints on signaling network logic reveal functional subgraphs on Multiple Myeloma OMIC data

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BMC SYSTEMS BIOLOGY 2018年第3-Sup期12卷 32-32页

作者： Miannay, Bertrand Minvielle, Stephane Magrangeas, Florence Guziolowski, Carito Ecole Cent Nantes LS2N UMR 6004 Nantes France Univ Nantes Univ Angers CNRS INSERMCRCINA Nantes France

Background: The integration of gene expression profiles (GEPs) and large-scale biological networks derived from pathways databases is a subject which is being widely explored. Existing methods are based on network distance measures among significantly measured species. Only a small number of them include the directionality and underlying logic existing in biological networks. In this study we approach the GEP-networks integration problem by considering the network logic, however our approach does not require a prior species selection according to their gene expression level. Results: We start by modeling the biological network representing its underlying logic using Logic programming. This model points to reachable network discrete states that maximize a notion of harmony between the molecular species active or inactive possible states and the directionality of the pathways reactions according to their activator or inhibitor control role. Only then, we confront these network states with the GEP. From this confrontation independent graph components are derived, each of them related to a fixed and optimal assignment of active or inactive states. These components allow us to decompose a large-scale network into subgraphs and their molecular species state assignments have different degrees of similarity when compared to the same GEP. We apply our method to study the set of possible states derived from a subgraph from the NCI-PID Pathway Interaction Database. This graph links Multiple Myeloma (MM) genes to known receptors for this blood cancer. Conclusion: We discover that the NCI-PID MM graph had 15 independent components, and when confronted to 611 MM GEPs, we find 1 component as being more specific to represent the difference between cancer and healthy profiles.

关键词： answer set programming Regulatory network modeling Omic data integration

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Syntactic ASP forgetting with forks

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ARTIFICIAL INTELLIGENCE 2024年 326卷

作者： Aguado, Felicidad Cabalar, Pedro Fandinno, Jorge Pearce, David Perez, Gilberto Vidal, Concepcion Univ A Coruna La Coruna Spain Univ Nebraska Omaha NE USA Univ Politecn Madrid Madrid Spain

answer set programming (ASP) constitutes nowadays one of the most successful paradigms for practical Knowledge Representation and declarative problem solving. The formal analysis of ASP programs is essential for a rigorous treatment of specifications, the correct construction of solvers and the extension with other representational features. In this paper, we present a syntactic transformation, called the unfolding operator, that allows forgetting an atom in a logic program (under ASP semantics). The main advantage of unfolding is that, unlike other syntactic operators, it is always applicable and guarantees strong persistence, that is, the result preserves the same stable models with respect to any context where the forgotten atom does not occur. The price for its completeness is that the result is an expression that may contain the fork operator. Yet, we illustrate how, in some cases, the application of fork properties may allow us to reduce the fork to a logic program.

关键词： answer set programming Equilibrium logic Forgetting Strong persistence Strong equivalence Forks

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Towards inductive learning of surgical task knowledge: a preliminary case study of the peg transfer task

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Procedia Computer Science 2020年 176卷 440-449页

作者： Daniele Meli Paolo Fiorini Mohan Sridharan Department of Computer Science University of Verona strada Le Grazie 15 Verona 37135 Italy School of Computer Science University of Birmingham Edgbaston Birmingham B152TT UK

Autonomy in robotic surgery will significantly improve the quality of interventions in terms of safety and recovery time for the patient, and reduce fatigue of surgeons and hospital costs. A key requirement for such autonomy is the ability of the surgical system to encode and reason with commonsense task knowledge, and to adapt to variations introduced by the surgical scenarios and the individual patients. However, it is difficult to encode all the variability in surgical scenarios and in the anatomy of individual patients a priori, and new knowledge often needs to be acquired and merged with the existing knowledge. At the same time, it is not possible to provide a large number of labeled training examples in the robotic surgery. This paper presents a framework based on inductive logic programming and answer set semantics for incrementally learning domain knowledge from a limited number of executions of basic surgical tasks. As an illustrative example, we focus on the peg transfer task, and learn state constraints and the preconditions of actions starting from different levels of prior knowledge. We do so using a small dataset comprising human and robotic executions with the da Vinci surgical robot in a challenging simulated scenario.

关键词： Incremental knowledge acquisition Surgical robotics Inductive logic programming answer set programming

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