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Cooperative Multi-agent Systems frameworks do not include modules to test communications yet. The proposed framework incorporates robust analysis tools using IDKAnalysis2.0 to evaluate bullying effect in communications. The present work is based on ICARO-T. This platform follows the Adaptive Multi-agent Systems paradigm. Experimentation with ICARO-T includes two deployments: the equitative and the authoritative. Results confirm the usefulness of the analysis tools when exporting to Cooperative Multi-agent Systems that use different configurations. Besides, ICARO-T is provided with new functionality by a set of tools for communication analysis.

Communication design is usually complex in Multi-Agent Systems (MAS) because of dynamic emergent behaviours. The lack of proper quantitative measures to assess alternative solutions and guide an iterative development makes this design even harder. The aim of this work is to efficiently find and describe communication patterns that should be avoided in these systems and identify the agents involved in these patterns. For this purpose, this research presents a suite of novel metrics and classification rules that, respectively, measure agents' communication and classify their results to describe patterns. This work also provides tools for automatically measuring the metrics and applying the classification rules. In order to evaluate this work, the results of applying these metrics and classification rules have been compared with the quality attribute of performance in several MAS. Performance is measured as the time between a user's request and the MAS response, and partially represents the factor of the quality of service. The experiments gather four agent-oriented communication designs that belong to two different domains: Crisis-management and Cinema ticket selling. The study reveals that the detected communication patterns are related with performance, and that the proposed metrics can arguably guide the design of communications improving the overall performance of systems.

Multi-agent executions become a difficult to follow task when there are lots of agents working concurrently. Programmers need to know if the system is working according to their expectations. In such situations, debugging tools are useful to follow the execution traces, but are not enough for such complex systems. An alternative solution is to save the execution traces (logs) in data warehouses, where they will be analysed through data mining techniques and queries. These techniques offer the programmer a summary of the execution parameters and even the discovery of unexpected patterns.

In large multi-agent system (MAS), the definition of protocols between pairs of agents is not enough for guaranteeing the absence of undesirable communication organizations and the presence of desirable ones. This paper presents a technique and a tool for designing robust MAS communication architectures. In particular, in the proposed technique, designers are not only recommended to design the desired communication protocols, but also the undesired ones and the organization structures. The proposed tool performs data mining and on-line analytical processing (OLAP) on the logs of execution of MAS that use the Foundation for Intelligent Physical Agents (FIPA) protocol, in order to debug MAS communication architectures. The experimental results advocate that this approach is useful for MAS debugging and testing.

Overworking behaviors appear in multi-agent systems communication quite often. This occurs when an agent receives many messages in a short period of time. As the agent pays attention to the large amount of messages it worsens its performance, causing the system’s performance to worsen as well. The reasons for this behavior are varied and depend on the nature of the messages: an agent that sends messages to the same agent when it should have sent it to a group of agents in a balanced way; an agent that does not receive a quick response becomes impatient and keep on sending the same message to the same agent until it gets a response, and so on. This article presents a technique that detects the overworked agents in INGENIAS with a new metric and efficiently measures it with a new version of the INGENIAS Development Kit. The experimentation of this technique advocates that the existence of overworked agents is strongly related with the quality of service of multi-agent systems.

The Digital Home is the result of the convergence of technologies of different nature that interact with each other in the Home environment. It is a realization of the Ambient Intelligence concept. The final objective of the Ambient Intelligence is that sensors, devices and networks that compose this environment can co-exist with human users, to improve their quality of live. The relevant characteristic of the Digital Home as a main scenario of Ambient Intelligence is its pervasive nature. This paper describes these technologies and their harmonization, based on the work done in the INREDIS project, which deals with accessibility and new technologies.