Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

Social learning in a multi-agent system

In a persistent multi-agent system, it should be possible for new agents to benefit from the accumulated learning of more experienced agents. Parallel reasoning can be applied to the case of newborn animals, and thus the biological literature on social learning may aid in the construction of effective multi-agent systems. Biologists have looked at both the functions of social learning and the mechanisms that enable it. Many researchers have focused on the cognitively complex mechanism of imitation; we will also consider a range of simpler mechanisms that could more easily be implemented in robotic or software agents. Research in artificial life shows that complex global phenomena can arise from simple local rules. Similarly, complex information sharing at the system level may result from quite simple individual learning rules. We demonstrate in simulation that simple mechanisms can outperform imitation in a multi-agent system, and that the effectiveness of any social learning strategy will depend on the agents' environment. Our simple mechanisms have obvious advantages in terms of robustness and design costs

Talking Nets: A Multi-Agent Connectionist Approach to Communication and Trust between Individuals

A multi-agent connectionist model is proposed that consists of a collection of individual recurrent networks that communicate with each other, and as such is a network of networks. The individual recurrent networks simulate the process of information uptake, integration and memorization within individual agents, while the communication of beliefs and opinions between agents is propagated along connections between the individual networks. A crucial aspect in belief updating based on information from other agents is the trust in the information provided. In the model, trust is determined by the consistency with the receiving agents’ existing beliefs, and results in changes of the connections between individual networks, called trust weights. Thus activation spreading and weight change between individual networks is analogous to standard connectionist processes, although trust weights take a specific function. Specifically, they lead to a selective propagation and thus filtering out of less reliable information, and they implement Grice’s (1975) maxims of quality and quantity in communication. The unique contribution of communicative mechanisms beyond intra-personal processing of individual networks was explored in simulations of key phenomena involving persuasive communication and polarization, lexical acquisition, spreading of stereotypes and rumors, and a lack of sharing unique information in group decisions