The science of complexity contains a plethora of models and ideas through which we can interpret and understand the world around us. Thus, we keep an eye on reality throughout, touching on the practical benefits of gaining an understanding of complex systems. Agent based modeling is a methodology that has been driving a lot of research in complex systems. In this work, we present how agent-base...

Nowadays the interest to the adaptive intelligent eLearning systems increases. There are different kind of adaptations one to the content, other to the learning process or to the assessment and so on. The crucial moment for the learner motivation’s is to catch their needs and possibilities and then to act, i.e. the respond from the system.. The intelligent tutor (system agent(s)) has to decide ...

It is shown that under certain conditions it is possible to model a complex system in a way that leads to results that do not depend on system size. As an example of complex system an innovation diffusion model is considered. In that model a set of individuals (the agents), which are interconnected, must decide if adopt or not an innovation. The agents are connected in a member of the networks ...

Building complex systems requires a specific kind of modularity as well as incremental development. Here we introduce an architecture where basic modules are reactive agents and the data exchange among them is based on the so-called stigmergic communication through space. In this way we have connected ideas coming from the multi-agent systems and the coordination languages on the one side and i...

INTRODUCTION For decades, the promise of computers that communicate with us through natural language has been depicted in works of science fiction and driven research agendas in artificial intelligence (AI) and human-computer interaction (HCI). As early as 1964, Joseph Weizenbaum demonstrated how a computer program could hold open-ended conversations using a large set of pattern matching rules ...

Organisations often form collaborative teams for sharing knowledge and resources in order to accomplish complex tasks. For example, a team involving non-governmental organisations, civil and military institutions may be formed rapidly in response to humanitarian crisis. In another context, a company may collaborate with a team of service providers in fulfilling its requirements. Collaboration e...

SCALING MULTI-AGENT LEARNING IN COMPLEX ENVIRONMENTS

We are interested in the physics of cyberspatial objects (events, agents, processes) whose behaviors unfold in cyberspace, a manifold where geospatial, infospatial and temporal indices are required to distinguish one object from another and, for a given object, one state from another. Consequently, we propose a cyberspace-time (CST) reference framework for describing the dynamics of cyberphysic...

We address the important practical issue of understanding, predicting and eventually controlling catastrophic endogenous changes in a collective. Such large internal changes arise as macroscopic manifestations of the microscopic dynamics, and their presence can be regarded as one of the defining features of an evolving complex system. We consider the specific case of a multi-agent system relate...

The instability of international financial markets, as exhibited by persistent periodic crises, is an important issue for all market participants. This paper presents an alternative agent-based network theory of financial crises derived from recent developments in complexity science, and applies it to short-term currency crises. The theory, despite being parsimonious, is capable of generating c...