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Branching-time temporal logics have proved to be an extraordinarily successful tool in the formal specification and verification of distributed systems. Much of their success stems from the tractability of the model checking problem for the branching time logic ctl, which has made it possible to implement tools that allow designers to automatically verify that systems satisfy requirements expressed in ctl. Recently, ctl was generalised by Alur, Henzinger, and Kupferman in a logic known as "Alternating-time Temporal Logic" (atl). The key insight in atl is that the path quantifiers of ctl could be replaced by "cooperation modalities", of the form where # is a set of agents. The intended interpretation of an atl formula is that the agents # can cooperate to ensure that # holds (equivalently, that # have a winning strategy for #). In this paper, we extend atl with knowledge modalities, of the kind made popular in the work of Fagin, Halpern, Moses, Vardi and colleagues. Combining these knowledge modalities with atl, it becomes possible to express such properties as "group # can cooperate to bring about # i# it is common knowledge in # that #". The resulting logic --- Alternating-time Temporal Epistemic Logic (atel) --- shares the tractability of model checking with its atl parent, and is a succinct and expressive language for reasoning about game-like multiagent systems.

Introduction This chapter presents the agent-based perspective on imitation. In this perspective, imitation is best considered as the behavior of an autonomous agent in relation to its environment, including other autonomous agents. We argue that such a perspective helps unfold the full potential of research on imitation and helps in identifying challenging and important research issues. We first explain the agent-based perspective and then discuss it in the context of particular research issues in studies with animals and artifacts, with reference to chapters presented in this book. At the end of the chapter we briefly introduce the individual contributions to this book and provide a roadmap that helps the reader in navigating through the exciting and highly interwoven themes that are presented in this book. In order to focus discussions, we explain the agent-based perspective with particular consideration of the correspondence

The paper introduces evolving fuzzy neural networks (EFuNNs) as a means for the implementation of the evolving connectionist systems (ECOS) paradigm that is aimed at building on-line, adaptive intelligent systems that have both their structure and functionality evolving in time. EFuNNs evolve their structure and parameter values through incremental, hybrid supervised/unsupervised, on-line learning. They can accommodate new input data, including new features, new classes, etc. through local element tuning. New connections and new neurons are created during the operation of the system. EFuNNs can learn spatial-temporal sequences in an adaptive way through one pass learning, and automatically adapt their parameter values as they operate. Fuzzy or crisp rules can be inserted and extracted at any time of the EFuNN operation. The characteristics of EFuNNs are illustrated on several case study data sets for time series prediction and spoken word classification. Their performance is compared with traditional connectionist methods and systems. The applicability of EFuNNs as general purpose on- line learning machines is discussed what concerns systems that learn from large databases, life-long learning systems, on-line adaptive systems in different areas of Engineering.

We are investigating techniques for developing distributed and adaptive collections of information agents that coordinate to retrieve, filter and fuse information relevant to the user, task and situation. In our system of agents, information gathering is seamlessly integrated with decision support. In this paper we present the distributed system architecture, agent collaboration interactions, and a reusable set of software components for structuring agents. The system has three types of agents: Interface agents interact with the user receiving user specifications and delivering results. They acquire, model, and utilize user preferences to guide system coordination in support of the user’s tasks. Task agents help users perform tasks by formulating problem solving plans and carrying out these plans through querying and exchanging information with other software agents. Information agents provide intelligent access to a heterogeneous collection of information sources. We have implemented this system framework and are developing collaborating agents in diverse complex real world tasks, such as organizational decision making, and financial portfolio management. 1

This paper describes work undertaken in the ADEPT (Advanced Decision Environment for Process Tasks) project towards developing an agent-based infrastructure for managing business processes. We describe how the key technology of negotiating, service providing, autonomous agents was realised and demonstrate how this was applied to the BT business process of providing a customer quote for network services. Issues of agent visualisation are also addressed. 1.

This paper presents and motivates an extended ontology knowledge model which represents semantic information about concepts explicitly. This knowledge model results from enriching the standard conceptual model with semantic information which precisely characterises the concept's properties and expected ambiguities, including which properties are prototypical of a concept and which are exceptional, the behaviour of properties over time and the degree of applicability of properties to subconcepts. This enriched conceptual model permits a precise characterisation of what is represented by class membership mechanisms and helps knowledge engineers to determine, in a straightforward manner, the meta-properties holding for a concept. Meta-properties are recognised to be the main tool for a formal ontological analysis that allows building ontologies with a clean and untangled taxonomic structure. This enriched semantics can prove useful to describe what is known by agents in a multi-agent systems, as it facilitates the use of reasoning mechanisms on the knowledge that instantiate the ontology. These mechanisms can be used to solve ambiguities that can arise when heterogeneous agents have to interoperate in order to perform a task.

Abstract. The paper introduces a new type of evolving fuzzy neural networks (EFuNNs), denoted as mEFuNNs, for on-line learning and their applications for dynamic time series analysis and prediction. mEFuNNs evolve through incremental, hybrid (supervised/unsupervised), on-line learning, like the EFuNNs. They can accommodate new input data, including new features, new classes, etc. through local element tuning. New connections and new neurons are created during the operation of the system. At each time moment the output vector of a mEFuNN is calculated based on the m-most activated rule nodes. Two approaches are proposed: (1) using weighted fuzzy rules of Zadeh-Mamdani type; (2) using Takagi-Sugeno fuzzy rules that utilise dynamically changing and adapting values for the inference parameters. It is proved that the mEFuNNs can effectively learn complex temporal sequences in an adaptive

Due to the enormous rowth of the World Wide Web, search engines have become indispensable tools for Web navigation. In order to provide powerful search facilities, search engines maintain comprehensive indices for documents and their contents on the Web by continuously downloading Web pages for processing. In this paper, we demonstrate an alternative, more efficient approach to the "download-first process-later" strategy of existing search engines by using mobile crawlers. The major advantage of the mobile approach is that the analysis portion of the crawling process is done locally where the data resides rather than remotely inside the Web search engine. This can significantly reduce network load which, in turn, can improve the performance of the crawling process.

The linguistic approach based on fuzzy sets has given very good results in the modeling of qualitative information, from what has been applied to solve several real-world problems in medicine, information retrieval, education, etc. In this paper, some practical considerations about the choice and semantic of the linguistic term set of such an approach, as well as different proposals of aggregation of linguistic information are studied. The Linguistic Weighted Averaging Operator to aggregate linguistic weighted information is described, and its use as a way of incorporating more flexibility in the information gathering and communication process among agents in a distributed intelligent agent model on the Internet is presented.