IST-1999-10304 - EEII Evolution and Ecology of Interacting Infohabitants

1. IST-1999-10304- EEIIEvolution and Ecology  of Interacting Infohabitants Imperial College of Science, Technology and Medicine Eindhoven University of Technology Universidade Nova de Lisboa

2. Main Goals • Study the ecology of a system of interacting intelligent agents, especially the scalability, openness, adaptability and stability of the ecosystem. • Define intelligent interaction • Study how the complexity of interaction of the agents influences the behaviour of the ecology. • Find what are the properties of the ecology of agents that are worth studying. • Study the openness with respect to integration of new types of intelligent agents. • Specify of conditions of stability of the ecosystem.

3. Methodology • WP1 & WP2 were foundational WPs. They defined the intelligent interaction, and the important properties of the ecology. • WP3-6 are the main packages with results (scalability, openness, adaptability, stability) • In order to develop better comprehensionof the fields we worked with simulations and performed experiments to explore the subjects extensively. • We worked on three different models: • Trading Scenario • Loads’ Transportation Scenario • Virus Scenario • Currently working on a demonstrator based on the trading scenario to illustrate our findings

4. EEII: Workpackage 3 - Scalability • Definition of Scalability • How much the capacity of a system to do useful work increases as the size of the system increases • Objectives • To determine the scalability of an ecosystem of intelligent agents. • To provide scaling laws for important variables, as well as their interpretation in a computer science context.

5. EEII: Workpackage 3 - Scalability • What has been done • The scalability of a population of agents that interact by trading with one another has been studied • The relationship between the number of agents and computational time has been investigated. • Methodology • Evolution: via genetic algorithm • Learning: via neural networks • Communication: via fuzzy logic

6. EEII: Workpackage 3 - Scalability • What is currently being done • Scalability of the three models is being investigated: computational time to run the simulation (written in Matlab code) is being studied • How does the computational time for a single agent depend on the number of agents (fixed number) • How does the growth in the population depend on the size of the population

7. EEII:Workpackage 4 – Openness • Objectives • Recognize and classify the openness-related propertiesof agent systems • Implement a prototype of an agent system with openness-related properties of several types • Investigate the influence of openness-related properties of implemented agent system upon efficiency of the system and its individual agents

8. EEII:Workpackage 4 – Openness • What has been done • A set of dimensions has been proposed to categorize openness of agent systems • Several important openness-related properties and capabilities of agent systems have been identified • A generalized definition of openness-related property or capability of agent system has been proposed • A generalized scheme for evaluation of openness-related properties and capabilities of agent systems has been developed • A prototype of an agent system has been implemented to be used for openness experiments

9. EEII:Workpackage 4 – Openness • What is currently being done • Conducting ecological experiments • Experiments with static system (fixed number of agents) • openness of agents toward new ontological information(with static and changing individual ontologies) • openness of agents toward new local conditions of different hosts • Experiments with changing system • openness of agents and the system as a whole toward changes in agent population • openness of agents and the system as a whole toward changes in local conditions of different hosts • Mixed experiments • Making conclusions about openness propertiesof the agent system

10. EEII: Workpackage 5 - Adaptation • Objective is to study adaptation in agent systems - A classification • Task Oriented Application • Agents are given tools so solve problems • Have memory and learning capabilities • Can categorise new situations • Interaction Oriented Application • Goal solving results from agent interactions • Multiple agents result in redundancy • Resembles a swarm system - most behaviour is hard-wired

11. EEII: Workpackage 5 - Adaptation • Combination of the two classes • Agents have the same tools as TOA's agents • Their goals are described in some conceptual space • Agents organise in clusters according to their goals • One concept has several clusters • The agent system acts similarly as an IOA • Redundancy where agents can find their partners • Stable to crashes, provides a “concept” directory • Each cluster is located in a single host

12. EEII: Workpackage 6 - Stability • Objectives • Propose a definition of stability in an ecology of agents context • Relate it / differentiate from stationarity • Verify this definition using simulations • Deduce necessary conditions for an ecosystem to be stable • What has been done • Conducted experiments with simulations • Proposed a definition of stability • Investigated conditions of stability and instability

13. EEII: Workpackage 6 - Stability • Model an ecology as astochastic process – a Markov process • A Markov process x1, x2, x3, x4, ... has a stationary distribution if, the joint probability distribution of xm is independent of the time index m. • A Markov process x1, x2, x3, x4, ... is stable if, the joint probability distribution of xm is independent of the time index m.

14. EEII: Workpackage 6 - Stability • Example “Games” • N players, toss a coin in pairs, the winner gives to the loser a unit of its wealth. Players are destroyed if their wealth is below 1 and generate another player if their wealth is more than 4. • 3 players, each can be in one of two states “+” or “-”. If two players are in the same state the third goes into that state. If all three players are in the same state, there is probability 0.9 that they all remain in that state and probability 0.1 that they all change state.

15. EEII: Summary • Evolution and Ecology of Interacting Infohabitants • Find what are the properties of the ecology of agents that are worth studying. • Study the scalability of an ecosystem of intelligent agents and provide scaling laws for important variables. • Recognize and classify the openness-related propertiesof agent systems and investigate their influence upon the efficiency of the system and its individual agents • Study adaptation in agent systems and provide classifications. • Define stability of agent systems and investigate the conditions under which a system becomes stable or unstable. • Currently working on a demonstrator based on the trading scenario to illustrate our findings

16. Questions