Can models of agents be transferred between different areas?

One of the main reasons for the sustained activity and interest in the field of agent-based systems, apart from the obvious recognition of its value as a natural and intuitive way of understanding the world, is its reach into very many different and distinct fields of investigation. Indeed, the notions of agents and multi-agent systems are relevant to fields ranging from economics to robotics, in contributing to the foundations of the field, being influenced by ongoing research, and in providing many domains of application. While these various disciplines constitute a rich and diverse environment for agent research, the way in which they may have been linked by it is a much less considered issue. The purpose of this panel was to examine just this concern, in the relationships between different areas that have resulted from agent research. Informed by the experience of the participants in the areas of robotics, social simulation, economics, computer science and artificial intelligence, the discussion was lively and sometimes heated

FEARLUS-G : A Semantic Grid Service for Land-Use Modelling

The project is supported by the UK Economic & Social Research Council (ESRC) under the “Pilot Projects in E-Social Science” programme (Award Reference: RES-149-25-0011).Postprin

Agent Based Modeling in Land-Use and Land-Cover Change Studies

Agent based models (ABM) for land use and cover change (LUCC) holds the promise to provide new insight into the processes and patterns of the human and biophysical interactions in ways that have never been explored. Advances in computer technology make it possible to run almost infinite numbers of simulations with multiple heterogeneously shaped actors that reciprocally interact via vertical and horizontal power lines on various levels. Based upon an extensive literature review the basic components for such exercises are explored and discussed. This resulted in a systematic representation of these components consisting of: (1) Spatial static input data, (2) Actor and Actor-group static input data, (3) Spatial dynamic input, (4) Actor and Actor-group dynamic input data, (5) the model with the rules describing the rules, (6) Spatial static output, (7) Actor and Actor-group static output, (8) Dynamic output of Actor behaviour changes, (9) Dynamic output of actor-group behavioural changes, (10) Dynamic output of spatial patterns, (11) Dynamic output of temporal patterns. This representation proves to be epistemologically useful in the analysis of the relationships between the ABM LUCC components. In this paper, this representation is also used to enumerate the strengths and limitations of agent based modelling in LUCC

Investigation into the effect of social learning in reinforcement learning board game playing agents

This thesis presents the use of social learning to improve the performance of game playing reinforcement learning agents. Agents are placed in a social learning environment as opposed to the Self-Play learning environment. Their performance is monitored and analysed in order to observe how the performance changes compared to Self-Play agents. Two case studies were conducted, one with the game Tic-Tac-Toe and the other with the African board game of Morabaraba. The Tic-Tac-Toe agents used a table based TD ( ) algorithm to learn the Q values. The results from the tests for the Tic-Tac-Toe agents indicate that the social learning agents perform better than the Self-Play agents in both board tests and competitive tests. By increasing the population sizes of the agents the number of superior social agents also increases as well as improvements in their skill level. In the second case study the agents use function approximation and the TD ( ) algorithm because of a larger number of states. The social agents performed better than the Self-Play agents in the board tests and are not superior in the test where they compete against each other. Larger populations were not possible with the Morabaraba agents but the results are still positive as the agents perform well in the board tests