The research results described are concerned with: - developing a domain modeling method and tools to provide the design and implementation of decision-making support systems for computer integrated manufacturing; - building a decision-making support system based on know-how and its software environment. The research is funded by NEDO, Japan.

In the late 90th, the complex of concepts, theories, technologies and software called knowledge-based systems has become a key point in development of many future-oriented manufacturing paradigms, such as Agile Manufacturing and Intelligent Manufacturing Systems. Besides, the progress from craft production, to automated and flexible production and to wards'next generation' production is now realized to be in many respects determined by the human/systems ability to handle the domain knowledge rather than simply by a given standard of knowledge in the domain. This is the motivation for a continuously growing research interest to utilization of manufacturing knowledge. It should be noted however, that while a great many reports on different theoretical and applied aspects of knowle dge utilization have been published, the issues of the specificity of manufacturing knowledge and the appropriateness of the methodologies brought into manufacturing from other domains to build knowledge-based systems have not been given due attention. One instance of this research lack is given in this paper with the phenomenon of know-how. It was discovered rather long ago that know-how plays an important role during the solving of professional tasks in manufacturing (e.g.

Through the Internet and the World-Wide Web, a vast number of information sources has become available, which offer information on various subjects by different providers, often in heterogeneous formats. This calls for tools and methods for building an advanced information-processing infrastructure. One issue in this area is the selection of suitable information sources in query answering. In this paper, we present a knowledge-based approach to this problem, in the setting where one among a set of information sources (prototypically, data repositories) should be selected for evaluating a user query. We use extended logic programs (ELPs) to represent rich descriptions of the information sources, an underlying domain theory, and user queries in a formal query language (here, XML-QL, but other languages can be handled as well). Moreover, we use ELPs for declarative query analysis and generation of a query description. Central to our approach are declarative source-selection programs, for which we define syntax and semantics. Due to the structured nature of the considered data items, the semantics of such programs must carefully respect implicit context information in source-selection rules, and furthermore combine it with possible user preferences. A prototype implementation of our approach has been realized exploiting the DLV KR system and its plp front-end for prioritized ELPs. We describe a representative example involving specific movie databases, and report about experimental results.

A model of an organism as an au tonomous intelligent system has been proposed. This model was used to analyz e learning of an organism in various environmental conditions. Processes of learning were divided into two types: strong and weak processes taking place in the absence an d the presence of aprioristic information about an object respectively. Weak lear ning is synonymous to adaptation when aprioristic programs already available in a system (an organism) are started. It was shown that strong learning is impossible fo r both an organism and any autonomous intelligent system. It was shown also that the knowledge base of an organism cannot be updated. Therefore, all behavior programs of an organism are congenital. A model of a conditioned reflex as a series of consecutive measurements of environmental parameters has been advanced. Repeated measurements are necessary in this case to reduce the error during decision making.

Extraction of association rules is widely used as a data mining method. However, one of the limit of this approach comes from the large number of extracted rules and the difficulty for a human expert to deal with the totality of these rules. We propose to solve this problem by structuring the set of rules into hierarchy. The expert can then therefore explore the rules, access from one rule to another one more general when we raise up in the hierarchy, and in other hand, or a more specific rules. Rules are structured at two levels. The global level aims at building a hierarchy from the set of rules extracted. Thus we define a first type of rule-subsomption relying on Galois lattices. The second level consists in a local and more detailed analysis of each rule. It generate for a given rule a set of generalization rules structured into a local hierarchy. This leads to the definition of a second type of subsomption. This subsomption comes from inductive logic programming and integrates a terminological model.

It is well known that the standard model of knowledge based on possible worlds is subject to the problem of logical omniscience, that is, the agents know all the logical consequences of the ir knowledge [Fagin, Halpern, Moses, and V ardi 1995, Chapter 9]. Thu s, possible-world definitions of knowledge make it difficult to reason about the knowledge tha t agents need to explicitly compute in order to make decisions and perform actions, or to capture si tuations where agents want to reason about the knowledge that other agents need to explicitly com pute in order to perform actions. This observation leads to a distinction between two forms of knowledge, implicit knowledge and explicit knowledge (or resource-bounded knowledge), a distinction long recog nized [Rosenschein 1985]. The classical AI approach known as the interpreted symbolic structures approach, where knowledge is based on information stored in data structures of the agent, can be seen as an instance of explicit knowledge. In contrast, the situated automata approach, which interprets knowledge based on information carried by the state of the machine, can be seen as an instance of implicit knowledge. Levesque [1984] makes a similar distinction bet ween implicit belief and explicit belief. While the possible-worlds approach is taken as the standard model for implicit knowledge, there is no standard model for explicit knowledge.

In this work we introduce a defeasible logic programming recommender server that accepts different types of queries from client agents that can be distributed in remote hosts. We formalize new ways of querying recommender servers containing specific information or preferences, and creating a particular context for the queries. This special type of queries (called contextual queries) allows recommender servers to compute recommendations for any client using its preferences, and will be answered using an argumentative inference mechanism. We focus on a particular implementation of recommended systems that extends the integration of argumentation and recommender systems to a multi-agent setting. Our approach is based on a DeLP-server that can answer queries from agents in remote hosts. Since client agents can consult different domain specific recommender servers, then, multiple configurations of clients and servers can be defined.

In this work an AI/ICT Platform is presented, to develop cognitive networks to cope with a management of a great availability of data and a necessity to dispose of prompt right information, extracted by data. In fact, the better strategic decision arise by a prompt availability of target and effective information. A cognitive network, and in particular an intelligent grid, helps to reach this goal. This intelligent grid allows to integrate many data source to drive analytics which transform data into useful information to support advanced operational control and strategic decision making. To realize an intelligent grid, it is necessary, firstly, capturing Knowledge, transforming data in information and introducing the knowledge in ICT framework and in Real-Time Systems. This is the right way to have a set of target and suitable information by using to take a correct decision, especially in real-time problem. So, in this work XBASE Cognitive Mapping Tool is presented. This tool allows to develop an intelligent grid, to support and “automate” strategic decision and so, to solve, also in real-time, every kind of problems. In particular, an application of this tool is presented, in monitoring of wastewater, the “BATTLE” Project.

GLAIR (Grounded Layered Architecture with Integrated Reasoning) is a multi-layered cognitive architecture for embodied agents operating in real,virtual, or simulated environments containing other agents. The highest layer of the GLAIR Architecture, the Knowledge Layer (KL), contains the beliefs of the agent, and is the layer in which conscious reasoning, planning, and act selection is performed. The lowest layer of the GLAIR Architecture, the Sensori-Actuator Layer (SAL), contains the controllers of the sensors and effectors of the hardware or software robot. Between the KL and the SAL is the Perceptuo-Motor Layer (PML), which grounds the KL symbols in perceptual structures and subconscious actions, contains various registers for providing the agent's sense of situatedness in the environment, and handles translation and communication between the KL and the SAL. The motivation for the development of GLAIR has been "Computational Philosophy", the computational understanding and implementation of human-level intelligent behavior without necessarily being bound by the actual implementation of the human mind. Nevertheless, the approach has been inspired by human psychology and biology.

Knowledge acquisition (KA) is essential to creating effective virtual healthcare systems. KA is typically done with expert users such as clinicians and psychologists. In this paper, we describe knowledge acquisition activities which we carried out with healthcare consumers, in the context of a project to generate English summaries of medical data about babies in a neonatal intensive care unit. Working directly with consumers was in many ways more challenging than working with medical professionals, but it did lead to valuable insights which benefited our projects. We hope that the discussion of our experiences will help other researchers who wish to conduct KA with healthcare consumers.