Argumentation-Based Information Exchange in Prediction Markets
Topology and memory effect on convention emergence.
Publication Type:
Conference PaperSource:
IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2009), Milan, Italy (2009)Abstract:
Social conventions are useful self-sustaining protocols for groups to
coordinate behavior without a centralized entity enforcing
coordination. We perform an in-depth study of different network
structures, to compare and evaluate the effects of different network
topologies on the success and rate of emergence of social
conventions. While others have investigated memory for learning
algorithms, the effects of memory or history of past activities on the
reward received by interacting agents have not been adequately
investigated. We propose a reward metric that takes into
consideration the past action choices of the interacting agents. The
research question to be answered is what effect does the history based
reward function and the learning approach have on convergence time to
conventions in different topologies. We experimentally investigate the
effects of history size, agent population size and neighborhood size
or the emergence of social conventions.
Effects of interaction history and network topology on rate of convention emergence.
Publication Type:
Conference PaperSource:
3rd International Workshop on Emergent Intelligence on Networked Agents (WEIN’09), p.13-19 (2009)Abstract:
Social conventions are useful self-sustaining protocols for groups to coordinate behavior without a centralized entity enforcing coordination. The emergence of such conventions in different multi agent network topologies has been investigated by several researches. Although we will perform an exhaustive study of different network structures, we are concerned that different topologies will affect the emergence in different ways. Therefore, the main research question in this work is comparing and studing effects of different topologies on the emergence of social conventions. While others have investigated memory for learning algorithms, the effects of memory on the reward have not been investigated thoroughly. We propose a reward metric that is derived directly from the history of the interacting agents. The reward metric is the majority rule, thus the emerging convention becomes self propagating in the society. Agents are proportionally rewarded based upon their conformity to the majority action when interacting with another agent. Another research question to be answered is what effect does the history based reward function have on convergence time in different topologies. We also investigate the effects of history size, agent population size and neighborhood size proving their effects by agent-based experimentation.
Case-Based Multiagent Reinforcement Learning: Cases as Heuristics for Selection of Actions
Should I Trust my Teammates? An experiment in Heuristic Multiagent Reinforcement Learning
Publication Type:
Conference PaperSource:
IJCAI Workshop on Grand Challenges for Reasoning from Experiences, IJCAI, Los Angeles, California, p.11-15 (2009)Keywords:
Reinforcement Learning; Multiagent Learning