Distributed Agents

In an attempt to illustrate the application of cognitive science principles to hard AI problems in machine learning we propose the LIDA technology, a cognitive science based architecture capable of more human-like learning. A LIDA based software agent or cognitive robot will be capable of three fundamental, continuously active, humanlike learning mechanisms: 1) perceptual learning, the learning of new objects, categories, relations, etc., 2) episodic learning of events, the what, where, and when, 3) procedural learning, the ...

Organizations and enterprises have developed complex data and information exchange systems that are now vital for their daily operations. Currently available systems, however, face a major challenge. On todays global information infrastructure, data semantics is more and more context-and time-dependent, and cannot be fixed once and for all at design time. Identifying emerging relationships among previously unrelated information items (eg, during data interchange) may dramatically increase their business value. This chapter introduce ...

Currently, operators try to improve their operat- ing situation under the changing production circumstances. Their operations are some kinds of empirical fruits of man- ufacturing experiences. It is an important problem in the fu- ture to maintain and to extend their technologies having been built up. For the purpose, methods and systems for techni- cal inheritance of advanced techniques like

A distributed management architecture is proposed for Internet-scale, open, distributed agent middleware systems. The management architecture presented supports the autonomy of both agents and middleware resources, incorporating an agent-initiated contract negotiation model for resource allocation and access. A leasing mechanism infrastructure designed and implemented for this purpose is presented.

We introduce a novel view on how to deal with the problems of semantic interoperability in distributed systems. This view is based on the concept of emergent semantics, which sees both the representation of semantics and the discovery of the proper interpretation of symbols as the result of a self-organizing process performed by distributed agents exchanging symbols and having utilities dependent on the proper interpretation of the symbols. This is a complex systems perspective on the problem of dealing with semantics. ...

This paper describes a framework for cooperation suitable for being exploited in a society of cooperative distributed agents. We briefly present a tool UPShell University of Porto Shell, which may be used to generate specific Intelligent Systems, although its main purpose is to build up a community of coarse-grain, semi-autonomous agents out either of those generated Intelligent Systems or of already pre-existent Knowledge Based Systems. After selecting the agents and the machines where each agent is going to run, UPShell sets up an architecture enabling agents to communicate and cooperate. Inside each agent, a blackboard-based architecture supports a flexible control and natural integration of different modules responsible for internal activity (problem solving) as well as external activity (cooperation and communication).

A local culture denotes a commonly shared behaviour within a cluster of firms. Similar to social norms or conventions, it is an emergent feature resulting from the firms' interaction in an economic network. To model these dynamics, we consider a distributed agent population, representing e.g. firms or individuals. Further, we build on a continuous opinion dynamics model with bounded confidence ($\epsilon$), which assumes that two agents only interact if differences in their behaviour are less than $\epsilon$. Interaction results in more similarity of behaviour, i.e. convergence towards a common mean. This framework is extended by two major concepts: (i) The agent's in-group consisting of acquainted interaction partners is explicitly taken into account. This leads to an effective agent behaviour reflecting that agents try to continue to interact with past partners and thus to keep sufficiently close to them. (ii) The in-group network structure changes over time, as agents can form new links to other agents with sufficiently close effective behaviour or delete links to agents no longer close in behaviour. Thus, our model provides a feedback mechanism between the agents' behaviour and their in-group structure. Studying its consequences by means of agent-based computer simulations, we find that for narrow-minded agents (low $\epsilon$) the additional feedback helps to find consensus more often, whereas for open-minded agents (high $\epsilon$) this does not hold. This counterintuitive result is explained by simulations of the network evolution.

... Akash System Agni System System Vayu -system system pet-system Fig.3 Simulation model for mobile agent platform findmemory agent: INPUT: nil OUTPUT: The memory available at protocol main memory: 1684 kb secondary memory: /dev/hda2 947447 kb /dev/hdb1 176350 ...

One of the challenges facing us when we try to use commercial materials in the classrooms, ie video games at the moment, is to identify appropriate strategies of collaboration with designers, creators and distribution agents. Recently, we are working on a collaborative ...