There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

An overview of AspectJ

[서평]「Component Software」 - Beyond Object-Oriented Programming -

The Study of Instinct

The current practice in multiagent systems typically associates the environment with resources that are external to agents and their communication infrastructure. Advanced uses of the environment include infrastructures for indirect coordination, such as digital pheromones, or support for governed interaction in electronic institutions. Yet, in general, the notion of environment is not well defined. Functionalities of the environment are often dealt with implicitly or in an ad hoc manner. This is not only poor engineering practice, it also hinders engineers to exploit the full potential of the environment in multiagent systems.

In this paper, we put forward the environment as an explicit part of multiagent systems.We give a definition stating that the environment in a multiagent system is a first-class abstraction with dual roles: (1) the environment provides the surrounding conditions for agents to exist, which implies that the environment is an essential part of every multiagent system, and (2) the environment provides an exploitable design abstraction for building multiagent system applications. We discuss the responsibilities of such an environment in multiagent systems and we present a reference model for the environment that can serve as a basis for environment engineering. To illustrate the power of the environment as a design abstraction, we show how the environment is successfully exploited in a real world application. Considering the environment as a first-class abstraction in multiagent systems opens up new horizons for research and development in multiagent systems.

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Environment as a first class abstraction in multiagent systems

Modeling the environment and agent-environment relationships is not well explored in multi-agent systems, in particular not for software multi-agent systems. This paper aims to contribute with a generic model for active perception in situated multi-agent systems. Active perception enables an agent to direct its perception at the most relevant aspects in the environment, according to its current task. The model decomposes perception into three functionalities: sensing, interpreting, and filtering. The agent first senses its neighborhood through a set of selected foci, resulting in a representation. A set of perceptual laws enforces domain specific constraints on sensing. Next, the agent interprets the representation by means of descriptions, resulting in a percept. Percepts are expressions that can be understood by the internal machinery of the agent. Finally, the percept is filtered by a set of selected filters, restricting the perceived data according to specific context relevant selection criteria.

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Towards active perception in situated multi-agent systems

In order to cope with today's dynamic environment, the described manufacturing control system is designed as a self-organising multi-agent system. The design of this novel system implements the PROSA reference architecture [1]. Coordination among agents is done indirectly through a pheromone-based dissipative field as is done by social insects in coordinating their behaviour. In this case, our agents act as social insects interpreting the pheromones put by the others in the environment. This control system is built from the basic elements of any manufacturing controller, namely products, resources and orders. However, the overall control system is constructed not only from those basic elements but also employing the appropriate interaction patterns among the agents who represent them. For coordination purposes, the agents send out a kind of mobile agents -- artificial ants -- to lay down information on the environment. In our case, where fulfilling the manufacturing orders is the main concern, there are at least 3 types of ant in this system: (1) feasibility ants -- to propagate information concerning the feasible finishing routes; (2) exploring ants -- to explore the feasible routes; and (3) intention ants -- to propagate the route preferences. The overall mechanism enables the system to exhibit a self-organising behaviour.

Self-organising in multi-agent coordination and control using stigmergy

Our own experience in developing agents and multi-agent systems painfully taught us that there are no instruments to support the implementation of such systems. A thorough literature study confirms the fact that implementation issues, which are definitely non-trivial but clearly underestimated, are mostly achieved in an ad hoc fashion. This obviously results in people reinventing solutions for common problems. In this paper, we propose agent implementation patterns as an instrument to describe problems and generic solutions for implementing agents and multi-agent systems. Agent implementation patterns contrast with other proposals of agent patterns in that the latter typically deal with architectural and conceptual models, and not with the actual implementation of agent characteristics. If we eventually want to come to a best practice for the development and implementation of agent systems, system developers should share their knowledge and experience gained during the implementation of their systems. Agent implementation patterns can contribute to formalize and share such knowledge.

Agent implementation patterns

Engineering non-trivial open multi-agent systems is a challenging task. Our research focusses on situated multi-agent systems, i.e. systems in which agents are explicitly placed in an environment which agents can perceive and in which they can act. Situated agents do not use long-term planning to decide what action sequence should be executed, but select actions based on the locally perceived state of the world and limited internal state. To cope with change and dynamism of the system, situated agents must be able to adapt their behavior. A well-known family of agent architectures for adaptive behavior are free-flow architectures. However, building a free-flow architecture based on an analysis of the problem domain is a quasi-impossible job for non-trivial agents. To tackle the complexity of designing adaptive agent behavior based on a free-flow architecture, suitable abstractions are needed to describe and structure the agent behavior. The abstraction of a role is obviously essential in this respect. A modeling language is needed as well to model the behavior of the agents. We propose a statechart modeling language to support the design of roles for situated agents. In this paper we describe a design process for adaptive behavior of situated agents as part of a multi-agent oriented methodology. The design process integrates the abstraction of a role with a free-flow architecture. Starting from the results of analysis of the problem domain, the designer incrementally refines the model of the agent behavior. The resulting class diagram serves as a basis for implementation. We illustrate the subsequent design steps with a case study on controlling a collection of automated guided vehicles.

/pdf/a-design-process-for-adaptive-behavior-of-situated-agents-4shmxp0rps.pdf

A design process for adaptive behavior of situated agents

In this paper we introduce a model for direct communication in situated multi-agent systems. Direct communication is typically associated with cognitive agents, where the information encoded in the messages is related to a mental state. This generally assumed view on communication however, does not fit the approach of situated, behavior-based agents. We propose a protocol-based communication model for situated agents. Communication specified in terms of protocols, i.e. well-defined sequences of messages, shifts the focus of communication from the reasoning upon messages towards the relationship between the exchanged messages. The model decomposes communication into three functional modules: message decoding, communicating and message encoding. The core of the model, the communicating module (1) interprets decoded messages and reacts to them in accordance with the applicable protocol, and (2) initiates or continues conversations when the conditions imposed by the applicable protocol are satisfied.

Elke Steegmans

Papers

Towards active perception in situated multi-agent systems

Self-organising in multi-agent coordination and control using stigmergy

Agent implementation patterns

A design process for adaptive behavior of situated agents

Protocol Based Communication for Situated Multi-Agent Systems