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A hand-designed internal representation of the world cannot deal with unknown or uncontrolled environments. Motivated by human cognitive and behavioral development, this paper presents a theory, an architecture, and some experimental results for developmental robotics. By a developmental robot, we mean that the robot generates its "brain" (or "central nervous system," including the information processor and controller) through online, real-time interactions with its environment (including humans). A new Self-Aware Self-Effecting (SASE) agent concept is proposed, based on our SAIL and Dav developmental robots. The manual and autonomous development paradigms are formulated along with a theory of representation suited for autonomous development. Unlike traditional robot learning, the tasks that a developmental robot ends up learning are unknown during the programming time so that the task-specific representation must be generated and updated through real-time "living" experiences. Experimental results with SAIL and Dav developmental robots are presented, including visual attention selection, autonomous navigation, developmental speech learning, range-based obstacle avoidance, and scaffolding through transfer and chaining.

It is customary to assume that agents receive information from the environment through their sensors. It is equally customary to assume that an agent is capable of information processing and thus of computation. These two assumptions may be misleading, particularly because so much basic theoretical work relies on the concepts of information and computation. In similarity with Dennett's intentional stance, I suggest that a lot of discussions in cognitive science, neuroscience and artificial intelligence is biased by a naïve notion of computation resulting from the adoption of a computational stance. As a case study, I will focus on David Chalmers' view of computation in cognitive agents. In particular, I will challenge the thesis of computational sufficiency. I will argue that computation is no more than the ascription of an abstract model to a series of states and dynamic transitions in a physical agent. As a result, computation is akin to center of masses and other epistemic shortcuts that are insufficient to be the underpinnings of a baffling-yet-physical phenomenon like consciousness.

We propose a stochastic dynamic model of migration and economic aggregation in a system of employed (immobile) and unemployed (mobile) agents which respond to local wage gradients. Dependent on the local economic situation, described by a production function which includes cooperative effects, employed agents can become unemployed and vice versa. The spatio-temporal distribution of employed and unemployed agents is investigated both analytically and by means of stochastic computer simulations. We find the establishment of distinct economic centers out of a random initial distribution. The evolution of these centers occurs in two different stages: (i) small economic centers are formed based on the positive feedback of mutual stimulation/cooperation among the agents, (ii) some of the small centers grow at the expense of others, which finally leads to the concentration of the labor force in different extended economic regions. This crossover to large-scale production is accompanied by an increase in the unemployment rate. We observe a stable coexistence between these regions, although they exist in an internal quasistationary non-equilibrium state and still follow a stochastic eigendynamics.

Agents for online trading purpose can be seen as a tool that helps computer users to purchase products from distributed resources based on their interests and preferences. One of the major features that determine the success of trading agent is the ability to negotiate with other agents, because most trading tasks involve interaction among agents. This paper presents a peer-to-peer multi-agent system architecture for online trading. The main objective of this system is to address some of the shortcomings that are present in contemporary online trading systems that focused on providing solutions for specific trading issues, such as single attribute-based negotiation, the requirement of an electronic marketplace and variations and status changes within the network. The proposed system architecture is a multi-tier, multi-agent architecture. The system architecture consists of three types of agents that are classified based on their functionality: interface, resource and retrieval agents. The interface agents are the front-end of the system and able to interact with different users to fulfill their needs. At the middle-tier, the resource agents access and capture the contents and the changes of the local information database. The retrieval agents are the back-end of the system and able to travel and interact with other agents at remote host machines. A prototype of this system is implemented using the IBM Aglet SDK.

Agent-based simulation is a powerful simulation modeling technique that has seen a number of applications in the last five years, including applications to real-world business problems. In this chapter I introduce agent-based simulation and review three applications to business problems: a theme park simulation, a stock market simulation, and a bankwide simulation.

Business process management is a challenging task that requires business processes being described, executed, monitored and continuously enhanced. This process management lifecycle requires business as well as IT people working together, whereby the view on business process is quite different on both sides. One important means for bridging the gap between both consists in having a modeling notation that can be easily understood but also has a precise semantics and can be used as a basis for workflow execution. Although existing approaches like BPMN and EPCs aim at being such as notation they are already very activity oriented and do not consider the underlying motivations of processes. Introducing the goal oriented process modeling notation (GPMN) a new language is presented that has the objective of bringing together both sides by establishing higher-level modeling concepts for workflows. This results in an increased intelligibility of workflow descriptions for business people and greater consideration for the way processes are described on the business side. The core idea of the approach consists in introducing different kinds of goals and goal relationships in addition to the established activity-centered behavior model. The applicability of the approach is further illustrated with an example workflow from Daimler AG.

We study a modelling framework and computational paradigm called Colonies of Synchronizing Agents (CSAs), which abstracts intracellular and intercellular mechanisms of biological tissues. The model is based on a multiset of agents (cells) in a common environment (a tissue). Each agent has a local contents, stored in the form of a multiset of atomic objects (e.g., representing molecules), updated by multiset rewriting rules which may act on individual agents (intracellular action) or synchronize the contents of pairs of agents (intercellular action).

In this paper we investigate dynamic properties of CSAs, by means of temporal logic, and we give a logical characterization of some notions inspired by biology such as robustness, mutants and species. We reveal the relation that exists between the concept of robustness for CSAs and the bisimulation relation on colonies. We also present some decidability results for particular cases of robustness.

The exchange of goods and services between bargaining software agents requires new forms of brokering mechanisms which achieve consensus between conflicting parties. Such mechanisms have to be designed in a way that they give rational self-interested agents no incentives for insincere behavior. We introduce an arbiter as third party that resolves conflicting bargaining situations between the agents. To achieve non-manipulative agent behavior, we investigate three arbitration protocols that avoid different forms of manipulations and show how each trades net efficiency for robustness against manipulations. We describe the applicability of the protocols in bilateral bargaining situations and, analyze their robustness against manipulations analytically and by simulations. We compare the protocols with Nash's arbitration¹ and the Groves-Clarke tax² and characterize situations in which our protocols are superior.

In distributed systems, traders mediate between clients and service providers. This paper introduces a trading model, which supports multiagent systems (MAS) and goes beyond simple trading in three ways: (a) Service composition — The trader composes complex services of the current service offers. During the composition, it checks the availability of the service offers. (b) Use of group agents — Group agents represent a group of agents with their individual policies and other context information. The trader can use the group agent's information for a pre-selection of service offers. (c) Adaptability — The trading model uses the notion of clients' trust into services and adapts to the clients' preferences and system policies. The trading model is used in a Computer-Supported Cooperative Work (CSCW) application, in which the trader finds adequate communication services for project teams with geographically distributed members.

Distributed database techniques and the Internet provide producers of statistics with a means to publish their data and metadata widely and make them available to a variety of users. Data matching to a user query and data access as well as data harmonization are some of the problems that should be solved. Intelligence is required in various stages of query answering and data matching. Moreover, the breadth and distributed nature of the Internet urge for a distributed approach. Agents seem to be the means by which both intelligence and distributed processing can be achieved. This paper presents a distributed approach for answering queries on statistical data that exist over the Internet using a multi-agent framework.

A digital city is a social information infrastructure for urban life (including shopping, business, transportation, education, welfare and so on). We started a project to develop a digital city for Kyoto based on the newest technologies including cooperative information agents. This paper presents an architecture for digital cities and shows the roles of agent interfaces in it. We propose two types of cooperative information agents as follows: (a) the front-end agents determine and refine users' uncertain goals, (b) the back-end agents extract and organize relevant information from the Internet, (c) Both types of agents opportunistically cooperate through a blackboard. We also show the research guidelines towards social agents in digital cities; the agent will foster social interaction among people who are living in/visiting the city.

For the generic specification of protocols, goals, or workflows, many approaches to agent-oriented modeling provide a concept of role. Roles abstract from the concrete agents involved in an interaction. They provide means for the evolution of agents and serve as components of agent design. Despite the widespread usage of roles in agent-oriented modeling, a systematic analysis of the different aspects and properties of this concept is still missing. In this paper, we perform such an analysis and identify requirements for a general role concept. We develop such a role concept for a modeling approach based on the UML and graph transformation systems and exemplify its use for the specification (and application) of protocols. Finally, we provide a run-time semantics for roles based on concepts from the theory of graph transformation.

This paper outlines a framework which supports the use of multiple perspectives in system development, and provides a means for developing and applying systems design methods. The framework uses "viewpoints" to partition the system specification, the development method and the formal representations used to express the system specifications. This VOSE (viewpoint-oriented systems engineering) framework can be used to support the design of heterogeneous and composite systems. We illustrate the use of the framework with a small example drawn from composite system development and give an account of prototype automated tools based on the framework.

Complex systems are of vast importance in the practical world as well as presenting many theoretical challenges. The measurement of system complexity is still imprecise. For many systems, their modular construction brings challenges in understanding how modules form and the emergent behavior which may result. In other systems, it is the development of encodings and communication protocols which allow complexity to increase dramatically. We take a broad view of these issues and then consider the nature of the system space which generates complexity. We show examples from cellular automata and applications of neural networks to data mining which suggest that complex systems often occupy simple structured sub-spaces. Finally, we look at the way modularity relates to networks and the implications for understanding human cognitive processing.