Modeling Decentralized Organizational Change in Honeybee Societies

Multi-agent organizations in dynamic environments, need to have the ability to adapt to environmental changes to ensure a continuation of proper functioning. Such adaptations can be made through a centralized decision process or come from the individuals within the organization. In the domain of social insects, such as honeybees and wasps, organizations are known to adapt in a decentralized fashion to environmental changes. An organizational model for decentralized organizational change is presented that can aid in analyzing and designing such organizations. The model is specified by dynamic properties at different aggregation levels. At the lowest level such properties characterize the behavior of individual roles, which can be related to higher level properties that express important elements such as survival of an organization. A honeybee colony is used as a case study

An Ambient Agent Model for Monitoring and Analysing Dynamics of Complex Human Behaviour

In ambient intelligent systems, monitoring of a human could consist of more complex tasks than merely identifying whether a certain value of a sensor is above a certain threshold. Instead, such tasks may involve monitoring of complex dynamic interactions between human and environment. In order to enable such more complex types of monitoring, this paper presents a generic agent-based framework. The framework consists of support on various levels of system design, namely: (1) the top level, including the interaction between agents, (2) the agent level, providing support on the design of individual agents, and (3) the level of monitoring complex dynamic behaviour, allowing the specification of the aforementioned complex monitoring properties within the agents. The approach is exemplified by a large case study concerning the assessment of driving behaviour, and is applied to two smaller cases as well (concerning fall detection of elderly, and assistance of naval operations, respectively), which are briefly described. These case studies have illustrated that the presented framework enables developers within ambient intelligence to build systems with more expressiveness regarding their monitoring focus. Moreover, they have shown that the framework is easy to use and applicable in a wide variety of domains. © 2011 - IOS Press and the authors. All rights reserved

Deliberate evolution in multi-agent systems

and their applications. SMC is sponsored by the Netherlands Organization for Scientific Research (NWO). CWI is a member o

A Cognitive Agent Model Using Inverse Mirroring for False Attribution of Own Actions to Other Agents

Abstract. This paper presents a cognitive agent model capable of showing situations where self-generated actions are attributed to other agents, as, for example, for patients suffering from schizophrenia. The mechanism underlying the model involves inverse mirroring: mapping preparation states onto sensory representations of observed actions. It is shown how this mechanism can develop based on Hebbian learning. The model provides a basis for applications to human-like virtual agents in the context of for example, training of therapists or agent-based generation of virtual stories