V. Abramov

Stability of multi-agent systems

This work attempts to shed light on the fundamental concepts behind the stability of multi-agent systems. We view the system as a discrete time Markov chain with a potentially unknown transitional probability distribution. The system will be considered to be stable when its state has converged to an equilibrium distribution. Faced with the non-trivial task of establishing the convergence to such a distribution, we propose a hypothesis testing approach according to which we test whether the convergence of a particular system metric has occurred. We describe some artificial multi-agent ecosystems that were developed and we present results based on these systems which confirm that this approach qualitatively agrees with our intuition.

Simulation of a trading multi-agent system

In a trading scenario agents interact with each other, selling and buying resources. In order to control the behavior of the trading scenario, the interactions must be coordinated. We present a brief discussion of communication types and coordination models applicable in multi-agent systems. We find a programmable tuple space more appropriate to manage and rule the interactions between the trading agents. We discuss the advantages of a trading agent model that deals with the trading strategy, concentrating on what to buy or sell. This relieves the agent from the task of coordinating the negotiations and their revoking or acceptances. This is the task of the programmable tuple space.

Contributions to adaptable agent societies

The adoption of agents as utile companions faces the problem of conciliating the development of complex and intelligent functionalities with the requirements of autonomy mobility and adaptability. Our main focus will be on the agents adaptability. A hybrid agent architecture approach is proposed where a static component, which resides at the users host and includes most of the intelligence and decision support capabilities, is complemented by a mobile component that is aimed at interacting with other agents. Some adaptation strategies, based on classical and fuzzy methodologies, are also discussed using as background scenario a trading market competitive environment with buyer and seller agents interacting in it.

Adapting populations of agents

We control a population of interacting software agents. The agents have a strategy, and receive a payoff for executing that strategy. Unsuccessful agents become extinct. We investigate the repercussions of maintaining a diversity of agents. There is often no economic rationale for this. If maintaining diversity is to be successful, i.e. without lowering too much the payoff for the non-endangered strategies, it has to go on forever, because the non-endangered strategies still get a good payoff, so that they continue to thrive, and continue to endanger the endangered strategies. This is not sustainable if the number of endangered ones is of the same order as the number of non-endangered ones. We also discuss niches, islands. Finally, we combine learning as adaptation of individual agents with learning via selection in a population.

A model for agent mobility and interaction

As information infrastructure move towards open systems where agents come and go, new facilities are required so that these agents can take advantage of each others functionalities. We need agent systems that can provide to newcomer agents a place and the right agent to interact with. Such functionality must cope with high rate of agent entrance, with high load of agents, with vanishing agents or nodes in the agent system. Given these requirements, agents are constantly facing a problem of deciding where to go and with whom to work with. These two decisions, pertaining to mobility and interaction, have been singled out as fundamental for every agent system. We present an algorithm targeted at these two decisions while it fulfils the aforementioned requirements.