The study of multi-agent systems (MAS) focuses on systems in which many intelligent agents interact with each other. These agents are considered to be autonomous entities such as software programs or robots. Their interactions can either be cooperative (for example as in an ant colony) or selfish (as in a free market economy). This book assumes only basic knowledge of algorithms and discrete maths, both of which are taught as standard in the first or second year of computer science degree programmes. A basic knowledge of artificial intelligence would useful to help understand some of the issues, but is not essential. The books main aims are: To introduce the student to the concept of agents and multi-agent systems, and the main applications for which they are appropriate To introduce the main issues surrounding the design of intelligent agents To introduce the main issues surrounding the design of a multi-agent society To introduce a number of typical applications for agent technology

An Introduction to MultiAgent Systems.

This chapter contains sections titled: 2.1 Introduction, 2.2 Agent Communications, 2.3 Agent Interaction Protocols, 2.4 Societies of Agents, 2.5 Conclusions, 2.6 Exercises, 2.7 References

Multiagent Systems and Societies of Agents

Our goal is to understand and build comprehensive agents that function effectively in challenging niches In particular, we identify a class of niches to be occupied by “adaptive intelligent systems (AISs)” In contrast with niches occupied by typical AI agents, AIS niches present situations that vary dynamically along several key dimensions: different combinations of required tasks, different configurations of available resources, contextual conditions ranging from benign to stressful, and different performance criteria We present a small class hierarchy of AIS niches that exhibit these dimensions of variability and describe a particular AIS niche, ICU (intensive care unit) patient monitoring, which we use for illustration throughout the paper To function effectively throughout the range of situations presented by an AIS niche, an agent must be highly adaptive In contrast with the rather stereotypic behavior of typical AI agents, an AIS must adapt several key aspects of its behavior to its dynamic situation: its perceptual strategy, its control mode, its choices of reasoning tasks to perform, its choices of reasoning methods for performing chosen tasks; and its meta-control strategy for global coordination of all its behavior We have designed and implemented an agent architecture that supports all of these different kinds of adaptation by exploiting a single underlying theoretical concept: An agent dynamically constructs explicit control plans to guide its choices among situation-triggered behaviors The architecture has been used to build experimental agents for several AIS niches We illustrate the architecture and its support for adaptation with examples from Guardian, an experimental agent for ICU monitoring

An architecture for adaptive intelligent systems

A software agent acts on behalf of someone to carry out a particular task which has been delegated to ~ t . Ln order to fulfill the interaction requirements of delegation, the agent needs to provide for collaborative natural language interaction added with the feature of multilingualism to facilitate a flexible form of interaction in the language required by the user. In the existing agents, the contributions from the fields of computational linguist~cs and multilingual computing are rendered to provide natural language interaction and multilingualism. But, the language ability requirements of an agent, considered from an agent perspective, necessitates a revamping in the requirements from both conceptual and architectural perspectives. The process of fulfillment of requirements inevitably leads to attributing two dimensions of language autonomy namely, language ability management autonomy and language behavior autonomy to the language ability of an agent. This results in an evolved form of language ability wh~ch is comparable to that of humans and hence termed as a language faculty. The two dimsns~ons of autonomy and the solutions found for other relevant issues are used in building the conceptual model of the language faculty. A new Belief, Task, Behavior paradigm for representlng the internal states of the language faculty 1s derived from the conceptual model. Based on this paradigm, a new Behavior Management Architecture for the language faculty of an agent is designed The architecture 1s supported with appropriate Role-Based design and Aspect-Oriented lmplementation models so that an agent with the required form of language abil~ty could be realized Using the architecture and its design and ~mplementation models, a Multilingual Natural language Agent Interface (MULLAI) for Email server 1s developed to illustrate the use of the solutions developed. The developed architecture is also extended in order to attribute a language faculty suitable for a multi-agent system, namely the Multilingual Multi-Agent System (MMAS). An evaluation of the agent with twodimensional language autonomy with the existlng multilingual d~aloguing agents is also performed. A secondary contribution of this work is that the developed solutions 1 models are generic in nature and not specific to a language faculty.

Agents With Two-Dimensional Language Autonomy: For Task Delegation

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Agents With Two-Dimensional Language Autonomy: For Task Delegation