5,529 research outputs found
A Study of AI Population Dynamics with Million-agent Reinforcement Learning
We conduct an empirical study on discovering the ordered collective dynamics
obtained by a population of intelligence agents, driven by million-agent
reinforcement learning. Our intention is to put intelligent agents into a
simulated natural context and verify if the principles developed in the real
world could also be used in understanding an artificially-created intelligent
population. To achieve this, we simulate a large-scale predator-prey world,
where the laws of the world are designed by only the findings or logical
equivalence that have been discovered in nature. We endow the agents with the
intelligence based on deep reinforcement learning (DRL). In order to scale the
population size up to millions agents, a large-scale DRL training platform with
redesigned experience buffer is proposed. Our results show that the population
dynamics of AI agents, driven only by each agent's individual self-interest,
reveals an ordered pattern that is similar to the Lotka-Volterra model studied
in population biology. We further discover the emergent behaviors of collective
adaptations in studying how the agents' grouping behaviors will change with the
environmental resources. Both of the two findings could be explained by the
self-organization theory in nature.Comment: Full version of the paper presented at AAMAS 2018 (International
Conference on Autonomous Agents and Multiagent Systems
Evolutionary games on graphs
Game theory is one of the key paradigms behind many scientific disciplines
from biology to behavioral sciences to economics. In its evolutionary form and
especially when the interacting agents are linked in a specific social network
the underlying solution concepts and methods are very similar to those applied
in non-equilibrium statistical physics. This review gives a tutorial-type
overview of the field for physicists. The first three sections introduce the
necessary background in classical and evolutionary game theory from the basic
definitions to the most important results. The fourth section surveys the
topological complications implied by non-mean-field-type social network
structures in general. The last three sections discuss in detail the dynamic
behavior of three prominent classes of models: the Prisoner's Dilemma, the
Rock-Scissors-Paper game, and Competing Associations. The major theme of the
review is in what sense and how the graph structure of interactions can modify
and enrich the picture of long term behavioral patterns emerging in
evolutionary games.Comment: Review, final version, 133 pages, 65 figure
Emergence of Self-Organized Symbol-Based Communication \ud in Artificial Creatures
In this paper, we describe a digital scenario where we simulated the emergence of self-organized symbol-based communication among artificial creatures inhabiting a \ud
virtual world of unpredictable predatory events. In our experiment, creatures are autonomous agents that learn symbolic relations in an unsupervised manner, with no explicit feedback, and are able to engage in dynamical and autonomous communicative interactions with other creatures, even simultaneously. In order to synthesize a behavioral ecology and infer the minimum organizational constraints for the design of our creatures, \ud
we examined the well-studied case of communication in vervet monkeys. Our results show that the creatures, assuming the role of sign users and learners, behave collectively as a complex adaptive system, where self-organized communicative interactions play a \ud
major role in the emergence of symbol-based communication. We also strive in this paper for a careful use of the theoretical concepts involved, including the concepts of symbol and emergence, and we make use of a multi-level model for explaining the emergence of symbols in semiotic systems as a basis for the interpretation of inter-level relationships in the semiotic processes we are studying
The emergence of reciprocally beneficial cooperation
This paper offers a new and robust model of the emergence and persistence of cooperation. In the model, interactions are anonymous, the population is well-mixed, and the evolutionary process selects strategies according to material payoffs. The cooperation problem is modelled as a game similar to Prisoner’s Dilemma, but there is an outside option of nonparticipation and the payoff to mutual cooperation is stochastic; with positive probability, this payoff exceeds that from cheating against a cooperator. Under mild conditions, mutually beneficial cooperation occurs in equilibrium. This is possible because the non-participation option holds down the equilibrium frequency of cheating.Cooperation; voluntary participation; random payoffs.
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Chris Cannings: A Life in Games
Chris Cannings was one of the pioneers of evolutionary game theory. His early work was inspired by the formulations of John Maynard Smith, Geoff Parker and Geoff Price; Chris recognized the need for a strong mathematical foundation both to validate stated results and to give a basis for extensions of the models. He was responsible for fundamental results on matrix games, as well as much of the theory of the important war of attrition game, patterns of evolutionarily stable strategies, multiplayer games and games on networks. In this paper we describe his work, key insights and their influence on research by others in this increasingly important field. Chris made substantial contributions to other areas such as population genetics and segregation analysis, but it was to games that he always returned. This review is written by three of his students from different stages of his career
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