3,814 research outputs found
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
Talking Helps: Evolving Communicating Agents for the Predator-Prey Pursuit Problem
We analyze a general model of multi-agent communication in which all agents communicate simultaneously to a message board. A genetic algorithm is used to evolve multi-agent languages for the predator agents in a version of the predator-prey pursuit problem. We show that the resulting behavior of the communicating multi-agent system is equivalent to that of a Mealy finite state machine whose states are determined by the agents’ usage of the evolved language. Simulations show that the evolution of a communication language improves the performance of the predators. Increasing the language size (and thus increasing the number of possible states in the Mealy machine) improves the performance even further. Furthermore, the evolved communicating predators perform significantly better than all previous work on similar preys. We introduce a method for incrementally increasing
the language size which results in an effective coarse-to-fine search that significantly reduces the evolution time required to find a solution. We present some observations on the effects of language size, experimental setup, and prey difficulty on the evolved Mealy machines. In particular, we observe that the start state is often revisited, and incrementally increasing the language size results in smaller Mealy machines. Finally, a simple rule is derived that provides a pessimistic estimate on the minimum language size that should be used for any multi-agent problem
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
Generalized Communicating P Systems Working in Fair Sequential Model
In this article we consider a new derivation mode for generalized
communicating P systems (GCPS) corresponding to the functioning of population
protocols (PP) and based on the sequential derivation mode and a fairness
condition. We show that PP can be seen as a particular variant of GCPS. We also
consider a particular stochastic evolution satisfying the fairness condition
and obtain that it corresponds to the run of a Gillespie's SSA. This permits to
further describe the dynamics of GCPS by a system of ODEs when the population
size goes to the infinity.Comment: Presented at MeCBIC 201
Coevolutionary dynamics of a variant of the cyclic Lotka-Volterra model with three-agent interactions
We study a variant of the cyclic Lotka-Volterra model with three-agent
interactions. Inspired by a multiplayer variation of the Rock-Paper-Scissors
game, the model describes an ideal ecosystem in which cyclic competition among
three species develops through cooperative predation. Its rate equations in a
well-mixed environment display a degenerate Hopf bifurcation, occurring as
reactions involving two predators plus one prey have the same rate as reactions
involving two preys plus one predator. We estimate the magnitude of the
stochastic noise at the bifurcation point, where finite size effects turn
neutrally stable orbits into erratically diverging trajectories. In particular,
we compare analytic predictions for the extinction probability, derived in the
Fokker-Planck approximation, with numerical simulations based on the Gillespie
stochastic algorithm. We then extend the analysis of the phase portrait to
heterogeneous rates. In a well-mixed environment, we observe a continuum of
degenerate Hopf bifurcations, generalizing the above one. Neutral stability
ensues from a complex equilibrium between different reactions. Remarkably, on a
two-dimensional lattice, all bifurcations disappear as a consequence of the
spatial locality of the interactions. In the second part of the paper, we
investigate the effects of mobility in a lattice metapopulation model with
patches hosting several agents. We find that strategies propagate along the
arms of rotating spirals, as they usually do in models of cyclic dominance. We
observe propagation instabilities in the regime of large wavelengths. We also
examine three-agent interactions inducing nonlinear diffusion.Comment: 22 pages, 13 figures. v2: version accepted for publication in EPJ
Instability of defensive alliances in the predator-prey model on complex networks
A model of six-species food web is studied in the viewpoint of spatial
interaction structures. Each species has two predators and two preys, and it
was previously known that the defensive alliances of three cyclically predating
species self-organize in two-dimensions. The alliance-breaking transition
occurs as either the mutation rate is increased or interaction topology is
randomized in the scheme of the Watts-Strogatz model. In the former case of
temporal disorder, via the finite-size scaling analysis the transition is
clearly shown to belong to the two-dimensional Ising universality class. In
contrast, the geometric or spatial randomness for the latter case yields a
discontinuous phase transition. The mean-field limit of the model is
analytically solved and then compared with numerical results. The dynamic
universality and the temporally periodic behaviors are also discussed.Comment: 5 page
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