The aim of this study is to formally express awareness for modeling practical agent communication. The notion of awareness has been proposed as a set of propositions for each agent, to which he/she pays attention, and has contributed to avoiding \textit{logical omniscience}. However, when an agent guesses another agent's knowledge states, what matters are not propositions but are accessible possible worlds. Therefore, we introduce a partition of possible worlds connected to awareness, that is an equivalence relation, to denote \textit{indistinguishable} worlds. Our logic is called Awareness Logic with Partition ($\mathcal{ALP}$). In this paper, we first show a running example to illustrate a practical social game. Thereafter, we introduce syntax and Kripke semantics of the logic and prove its completeness. Finally, we outline an idea to incorporate some epistemic actions with dynamic operators that change the state of awareness.

Natural language generation (NLG) is an important component in spoken dialogue systems.This paper presents a model called Encoder-Aggregator-Decoder which is an extension of an Recurrent Neural Network based Encoder-Decoder architecture.The proposed Semantic Aggregator consists of two components: an Aligner and a Refiner.The Aligner is a conventional attention calculated over the encoded input information, while the Refiner is another attention or gating mechanism stacked over the attentive Aligner in order to further select and aggregate the semantic elements.The proposed model can be jointly trained both text planning and text realization to produce natural language utterances.The model was extensively assessed on four different NLG domains, in which the results showed that the proposed generator consistently outperforms the previous methods on all the NLG domains.* A combination of an action type and a set of slot-value pairs).E.g. inform(name='Piperade'; food='Basque').

BACKGROUND: Relation extraction is a fundamental technology in biomedical text mining. Most of the previous studies on relation extraction from biomedical literature have focused on specific or predefined types of relations, which inherently limits the types of the extracted relations. With the aim of fully leveraging the knowledge described in the literature, we address much broader types of semantic relations using a single extraction framework. RESULTS: Our system, which we name PASMED, extracts diverse types of binary relations from biomedical literature using deep syntactic patterns. Our experimental results demonstrate that it achieves a level of recall considerably higher than the state of the art, while maintaining reasonable precision. We have then applied PASMED to the whole MEDLINE corpus and extracted more than 137 million semantic relations. The extracted relations provide a quantitative understanding of what kinds of semantic relations are actually described in MEDLINE and can be ultimately extracted by (possibly type-specific) relation extraction systems. CONCLUSION: PASMED extracts a large number of relations that have previously been missed by existing text mining systems. The entire collection of the relations extracted from MEDLINE is publicly available in machine-readable form, so that it can serve as a potential knowledge base for high-level text-mining applications.

Abstract: Ontologies are intended to facilitate semantic interoperability among distributed and intelligent information systems. Because of the distributed nature of the World Wide Web, Web ontologies have been developing in multiple forms of heterogeneity. For interoperating among information systems through heterogeneous ontologies, ontology mapping is a prerequisite process to generate alignment between two ontologies. In order to improve alignment accuracy, our approach is to clarify and enrich the semantics of ontological entities before mapping. For this purpose, we present a semi-automatic framework of entity classification and enrichment by applying three philosophical notions: identity condition, existential rigidity, and external dependency. Our objective is to supply a set of philosophy-driven anchors into ontologies for their mapping process by using a sortal taxonomy as a background knowledge model.

Matching between heterogeneous ontologies becomes crucial for interoperability in distributed and intelligent environments. Although many efforts in ontology mapping have already been conducted, most of them rely heavily on the meaning of entity names, rather than the semantics defined in ontologies. In order to deal with semantic heterogeneity, we propose a semantically enriched model of ontologies (called MetaOntoModel) where every domain concept is treated as a sort—an entity type that carries identity criteria for its instances—and classified these concepts based on three philosophical notions: identity, rigidity, and dependency. According to the classification, concept-level properties (called meta-knowledge) are embedded for each concept. Our novel idea is that if two concepts are semantically equivalent, then they have the same meta-knowledge. On the contrary, if two concepts possess different kinds of meta knowledge, then they cannot be matched. We also prove that meta-knowledge can determine not only the scope of semantic correspondences, but also which properties are the most relevant in finding correspondence between two similar concepts.

Our purpose is to propose postulates about operators which capture dynamic aspects on a legal argumentation, and to construct operators which satisfy the postulates and expand plaintiff and defendant's theories coherently. These ideas are the generalizations of Olsson's additive consolidation. Consolidation is studied in the field of belief revision, and makes an agent's epistemic state coherent. Therefore, our presentation will build a bridge between legal reasoning and belief revision.

Deductive object-oriented databases and situation theory are two important areas of research in the fields of databases and of linguistics. "AI and law" is a new field attracting both AI researchers and legal practitioners. Our research brings together the former two fields with the aim of designing knowledge applications in the latter. This is achieved through a formal model for legal reasoning, /spl Sscr//spl Mscr/ ("Situation-theoretic Model"), and a deductive object-oriented database system, /spl Qscr//spl Uscr//spl Iscr//spl Xscr//spl Oscr//spl Tscr//spl Escr/. The purpose of this paper is to introduce the key features of this formal model, based on situation theory, and to describe how this database system can implement this abstract model for complex legal reasoning applications. Concrete examples from legal precedents are used to illustrate these advanced features.

The new IL%?ZC-11 is a software tool for legal reasoning.It consists of two functions -argumentation function and debating function.Argumentation function is retilzed by a typed logic programming language with generalization of rules and defea.siblereasoning based on priority of rules.Debating function is realized by meta knowledge which controls the argumentation function.This paper introduces overview of the new HELIC-H system.We show how legal knowledge is represented in the new HELIC-11 illustrated by presenting the example of an actual criminzd case.

In advanced knowledge processing, partial information plays an important role in coping with complex data and knowledge. To handle such information, we developed the knowledge representation language (which may be regarded as a deductive object-oriented database language) QUIXOTE. The language has the features of both logic and object-orientation concepts, as well as database features. In this paper, we introduce the query processing mechanism for partial information databases in QUIXOTE, as a tool for effective data processing, taking the example of legal reasoning, and show its applicability to many applications in artificial intelligence.>

The authors developed a language QUIXOTE as a tool to deal with various information in natural language processing. QUIXOTE is a hybrid language of a deductive object-oriented database and constraint logic programming language. The new mechanism of QUIXOTE is a combination of an object-orientation concept such as object identity and the concept of a module that classifies a large knowledge base. In addition, its logical inference system is extended to be able to make restricted abduction. The authors first apply QUIXOTE to the sorted feature structure of constraint-based grammar formalisms. Next, it is shown that QUIXOTE can contribute to the description of situation-based semantics. The authors have implemented a system to make abductive reasoning to clarify hidden information. Also, they resolve the problem of noun phrase reference.