1,721,074 research outputs found
Special issue of the 24th RCRA InternationalWorkshop on Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion
No full textAttention, please! A critical review of neural attention models in natural language processing
No full textAttention is an increasingly popular mechanism used in a wide range of neural architectures. Because of the fast-paced advances in this domain, a systematic overview of attention is still missing. In this article, we define a unified model for attention architectures for natural language processing, with a focus on architectures designed to work with vector representation of the textual data. We discuss the dimensions along which proposals differ, the possible uses of attention, and chart the major research activities and open challenges in the area
Constraint detection in natural language problem descriptions
Full text linkModeling in constraint programming is a hard task that requires considerable expertise. Automated model reformulation aims at assisting a naive user in modeling constraint problems. In this context, formal specification languages have been devised to express constraint problems in a manner similar to natural yet rigorous specifications that use a mixture of natural language and discrete mathematics. Yet, a gap remains between such languages and the natural language in which humans informally describe problems. This work aims to alleviate this issue by proposing a method for detecting constraints in natural language problem descriptions using a structured-output classifier. To evaluate the method, we develop an original annotated corpus which gathers 110 problem descriptions from several resources. Our results show significant accuracy with respect to metrics used in cognate tasks
Special issue of the knowledge representation and reasoning track, symposium on applied computing (SAC) 2017: Preface
No full textSpecial issue of Teams in Multiagent Systems (TEAMAS): Preface
No full textAndrejczuk, E.; Alberola Oltra, JM.; Marcolino, L.; Torroni, P. (2020). Special issue of Teams in Multiagent Systems (TEAMAS): Preface. Fundamenta Informaticae. 174(1):61-62. https://doi.org/10.3233/FI-2020-1931S6162174
Detecting and explaining unfairness in consumer contracts through memory networks
Full text linkRecent work has demonstrated how data-driven AI methods can leverage consumer protection by supporting the automated analysis of legal documents. However, a shortcoming of data-driven approaches is poor explainability. We posit that in this domain useful explanations of classifier outcomes can be provided by resorting to legal rationales. We thus consider several configurations of memory-augmented neural networks where rationales are given a special role in the modeling of context knowledge. Our results show that rationales not only contribute to improve the classification accuracy, but are also able to offer meaningful, natural language explanations of otherwise opaque classifier outcomes
Neural-Symbolic Argumentation Mining: An Argument in Favor of Deep Learning and Reasoning
Full text linkDeep learning is bringing remarkable contributions to the field of argumentation mining, but the existing approaches still need to fill the gap toward performing advanced reasoning tasks. In this position paper, we posit that neural-symbolic and statistical relational learning could play a crucial role in the integration of symbolic and sub-symbolic methods to achieve this goal
Co-operation and competition in ALIAS: a logic framework for agents that Negotiate
No full textThis paper presents ALIAS, an agent architecture based on intelligent logic agents, where the main form of agent reasoning is abduction. The system is particularly suited for solving problems where knowledge is incomplete, where agents may need to make reasonable hypotheses about the problem domain and other agents, and where the raised hypotheses have to be consistent for the overall set of agents. ALIAS agents are pro-active, exhibiting a goal-directed behavior, and autonomous, since each one can solve problems using its own private knowledge base. ALIAS agents are also social, because they are able to interact with other agents, in order to cooperatively solve problems. The coordination mechanisms are modeled by means of LAILA, a logic-based language which allows to express intra-agent reasoning and inter-agent coordination. As an application, we show how LAILA can be used to implement inter-agent dialogues, e.g., for negotiation. In particular, LAILA is well-suited to coordinate the process of negotiation aimed at exchanging resources between agents, thus allowing them to execute the plans to achieve their goals
Interpreting abduction in CLP
No full textConstraint Logic Programming (CLP) and Abductive Logic Programming (ALP) share the important concept of conditional answer. We exploit their deep similarities to implement an efficient abductive solver where abducibles are treated as constraints. We propose two possible implementations, in which integrity constraints are exploited either (i) as the definition of a CLP solver on an abductive domain, or (ii) as constraints à la CLP. Both the solvers are implemented on top of CLP(Bool), that typically have impressively efficient propagation engines
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