A survey of agent-oriented methodologies

This article introduces the current agent-oriented methodologies. It discusses what approaches have been followed (mainly extending existing object oriented and knowledge engineering methodologies), the suitability of these approaches for agent modelling, and some conclusions drawn from the survey

Managing healthcare workflows in a multi-agent system environment

Whilst Multi-Agent System (MAS) architectures appear to offer a more flexible model for designers and developers of complex, collaborative information systems, implementing real-world business processes that can be delegated to autonomous agents is still a relatively difficult task. Although a range of agent tools and toolkits exist, there still remains the need to move the creation of models nearer to code generation, in order that the development path be more rigorous and repeatable. In particular, it is essential that complex organisational process workflows are captured and expressed in a way that MAS can successfully interpret. Using a complex social care system as an exemplar, we describe a technique whereby a business process is captured, expressed, verified and specified in a suitable format for a healthcare MAS.</p

Behavioral types in programming languages

A recent trend in programming language research is to use behav- ioral type theory to ensure various correctness properties of large- scale, communication-intensive systems. Behavioral types encompass concepts such as interfaces, communication protocols, contracts, and choreography. The successful application of behavioral types requires a solid understanding of several practical aspects, from their represen- tation in a concrete programming language, to their integration with other programming constructs such as methods and functions, to de- sign and monitoring methodologies that take behaviors into account. This survey provides an overview of the state of the art of these aspects, which we summarize as the pragmatics of behavioral types

OperA/ALIVE/OperettA

Comprehensive models for organizations must, on the one hand, be able to specify global goals and requirements but, on the other hand, cannot assume that particular actors will always act according to the needs and expectations of the system design. Concepts as organizational rules (Zambonelli 2002), norms and institutions (Dignum and Dignum 2001; Esteva et al. 2002), and social structures (Parunak and Odell 2002) arise from the idea that the effective engineering of organizations needs high-level, actor-independent concepts and abstractions that explicitly define the organization in which agents live (Zambonelli 2002).Peer ReviewedPostprint (author's final draft

Negotiating agents that learn about others’ preferences

In multiagent systems, an agent does not usually have complete information about the preferences and decision making processes of other agents. This might prevent the agents from making coordinated choices, purely due to their ignorance of what others want. This paper describes the integration of a learning module into a communication-intensive negotiating agent architecture. The learning module gives the agents the ability to learn about other agents&rsquo; preferences via past interactions. Over time, the agents can incrementally update their models of other agents&rsquo; preferences and use them to make better coordinated decisions. Combining both communication and learning, as two complement knowledge acquisition methods, helps to reduce the amount of communication needed on average, and is justified in situation where communication is computationally costly or simply not desirable (e.g. to preserve the individual privacy).<br /