The study and development of agent-based system model are conducted continuously. Agent technology can be used to solve and enhance the complex and ubiquity system by making the intelligent system [1]. Specially, BDI agent model is suit to dynamic and open system. Recent few years, functions and role of mobile devices are extended and also deployment becomes easier. And beyond the limitation of physical properties, mobile environment becomes complex and dynamic by integrating of functions and constraints of resources. Accordingly, on mobile environment, the study of agent-based intelligent system and application is required. In this paper, BDI Agent-based Service is proposed for developing intelligent mobile application. We use JAM agent platform and environment is Android.

In this paper, we discuss a Work Process Agent (WPA) that enables users to access a range of services for achieving their tasks with service composition based on planning in ubiquitous computing environment. WPA is a smart assistant application specializing in information service which needs to provide each user's own user model and make the model be adapted to the user's preferences dynamically. With WPA, users can achieve their task in a simple way. WPA employs a special BDI-based coordinator agent with planning for coordinating the individual WPA and external services. The plan-based service composition model works at abstraction level of process. This model makes it possible to reuse the frames of plans by making it independent from domain.

In this paper, we discuss the use of knowledge for analyzing and retrieving music contents semantically. First, we present Context-based Music Recommendation (COMUS) ontology to reason desired user emotion state from context and user preference information in the ontology. COMUS is a music dedicated ontology in OWL constructed by incorporating domain specific classes for music recommendation into the Music Ontology, which include situation, mood and musical features. More specifically, we describe the ontologies of mood and situation in music using low-level features like pitch or duration and musical factors like tempo or rhythm. Our proposed ontology defines generic as well as domain-specific concepts whose detection is important for the analysis and description of music in a specific domain. As a novelty, our ontology can express detailed and complicated relations among the music, moods and situations, enabling users to find appropriate music for the music retrieval and recommendation application. We present some of the experiments we performed as a case-study for music recommendation.

In cooperation systems such as Multi-Agent Systems, it is an important problem how group of agents is organized as well as how agents cooperate with each other. Moreover, dynamic computing environments require the dynamic organization to the cooperation systems. To this end, in this paper, we propose two organization algorithms based on trust and reputation of agents; one is a centralized and another is a distributed self-organization algorithm.

Query reformulation has been suggested as an effective way to improve retrieval efficiency in text information retrieval and one of the well-known techniques for query reformulation is user relevance feedback. Recently, there has been an increased interest in the query reformulation using relevance feedback with evolutionary techniques such as genetic algorithm for multimedia information retrieval. However, these techniques have still not been exploited widely in the field of music retrieval. In this paper, we propose a novel music retrieval scheme that is based on user relevance feedback with genetic algorithm and evolutionary method with neural network. The former is for reformulating a user query and the latter is for reducing the population size by learning neural network. We implemented a prototype music retrieval system called MUSEMBLE based on this scheme. Experimental results showed that our proposed scheme achieves a good performance.

Accurate voice humming transcription and efficient indexing schemes are essential for a large-scale humming-based music retrieval system. Although many researches have been done to develop such schemes, their performances are not still satisfactory. In our previous works, we proposed (i) a new voice query transcription scheme [4], (ii) a popularity-adaptive indexing structure called FAI [6] for fast retrieval, and (iii) a semi-supervised relevance feedback and query reformulation scheme based on a genetic algorithm [7] in order to improve retrieval efficiency. In this demonstration, we extend our efforts to a mobile environment and develop a prototype mobile music retrieval system called M-MUSICS. Our focus in this implementation includes versatile user interface for easy querying and browsing on a typical mobile device such as PDA phone and satisfactory performance in a wireless mobile environment. We report some of the results.

When processing raw documents in Information Retrieval (IR) System, a term-weighting scheme is used to calculate the importance of each term which occurs in a document. However, most term-weighting schemes assume that a term is independent of the other terms. Term dependency is an indispensable consequence of language use [1]. Therefore, this assumption can make the information of a document being lost. In this paper, we propose new approach to refine term weights of documents using term dependencies discovered from a set of documents. Then, we evaluate our method with two experiments based on the vector space model [2] and the language model [3].

In this paper, we propose a new approach for topic distillation on World Wide Web. Topic distillation is to find quality documents related to the user query topic. Our approach is based on Bharat's topic distillation algorithm [1]. We present the analysis of hyperlink graph structure using hierarchy concept tree to solve the mixed hubs problem that is also remained in the Bharat's algorithm. For assigning better weights to hyperlinks which point to relevant documents among hyperlinks in a document, we try to find the relationship in documents connected by hyperlinks using content analysis and we assign weights to hyperlinks based on the relationship. We evaluated this algorithm using 50 topics on WT10g corpus and obtained improved results.

Many intelligent retrieval approaches have been studied to bridge the terminological gap existing between the way in which users specify their information needs and the way in which queries are expressed. One of the approaches, called RUBRIC (RUle-Based Retrieval of Information by Computer), uses production rules to capture user query concepts (or topics). A set of related production rules is represented as an AND/OR tree, called a rule-based tree. One of the main problems in this approach is how to construct such rules that can capture user query concepts. This paper provides a logical framework that is semantically essential to defining the rules for the user query concepts, and proposes a way to automatically construct rule-based trees from typical thesauri. Experiments performed on small collections with a domain-specific thesaurus show that the automatically constructed rules are more effective than hand-made rules in terms of precision.

A semantic framework for nonmonotonic reasoning is developed. Specifically, a reliability measure theory based on multivalued logics that view a knowledge base as a set of contexts is proposed, and the contexts are compared according to some reliability measure. The proposed theory shows higher precision of nonmonotonic reasoning than other approaches. In particular, the qualification problem does not occur and many unwanted conflicts are eliminated.>