43,042 research outputs found
The Impact of Coevolution and Abstention on the Emergence of Cooperation
This paper explores the Coevolutionary Optional Prisoner's Dilemma (COPD)
game, which is a simple model to coevolve game strategy and link weights of
agents playing the Optional Prisoner's Dilemma game. We consider a population
of agents placed in a lattice grid with boundary conditions. A number of Monte
Carlo simulations are performed to investigate the impacts of the COPD game on
the emergence of cooperation. Results show that the coevolutionary rules enable
cooperators to survive and even dominate, with the presence of abstainers in
the population playing a key role in the protection of cooperators against
exploitation from defectors. We observe that in adverse conditions such as when
the initial population of abstainers is too scarce/abundant, or when the
temptation to defect is very high, cooperation has no chance of emerging.
However, when the simple coevolutionary rules are applied, cooperators
flourish.Comment: To appear at Studies in Computational Intelligence (SCI), Springer,
201
Imitative learning as a connector of collective brains
The notion that cooperation can aid a group of agents to solve problems more
efficiently than if those agents worked in isolation is prevalent, despite the
little quantitative groundwork to support it. Here we consider a primordial
form of cooperation -- imitative learning -- that allows an effective exchange
of information between agents, which are viewed as the processing units of a
social intelligence system or collective brain. In particular, we use
agent-based simulations to study the performance of a group of agents in
solving a cryptarithmetic problem. An agent can either perform local random
moves to explore the solution space of the problem or imitate a model agent --
the best performing agent in its influence network. There is a complex
trade-off between the number of agents N and the imitation probability p, and
for the optimal balance between these parameters we observe a thirtyfold
diminution in the computational cost to find the solution of the
cryptarithmetic problem as compared with the independent search. If those
parameters are chosen far from the optimal setting, however, then imitative
learning can impair greatly the performance of the group. The observed
maladaptation of imitative learning for large N offers an alternative
explanation for the group size of social animals
Prosocial dynamics in multiagent systems
Meeting today's major scientific and societal challenges requires understanding dynamics of prosociality in complex adaptive systems. Artificial intelligence (AI) is intimately connected with these challenges, both as an application domain and as a source of new computational techniques: On the one hand, AI suggests new algorithmic recommendations and interaction paradigms, offering novel possibilities to engineer cooperation and alleviate conflict in multiagent (hybrid) systems; on the other hand, new learning algorithms provide improved techniques to simulate sophisticated agents and increasingly realistic environments. In various settings, prosocial actions are socially desirable yet individually costly, thereby introducing a social dilemma of cooperation. How can AI enable cooperation in such domains? How to understand long-term dynamics in adaptive populations subject to such cooperation dilemmas? How to design cooperation incentives in multiagent learning systems? These are questions that I have been exploring and that I discussed during the New Faculty Highlights program at AAAI 2023. This paper summarizes and extends that talk
Coordination approaches and systems - part I : a strategic perspective
This is the first part of a two-part paper presenting a fundamental review and summary of research of design coordination and cooperation technologies. The theme of this review is aimed at the research conducted within the decision management aspect of design coordination. The focus is therefore on the strategies involved in making decisions and how these strategies are used to satisfy design requirements. The paper reviews research within collaborative and coordinated design, project and workflow management, and, task and organization models. The research reviewed has attempted to identify fundamental coordination mechanisms from different domains, however it is concluded that domain independent mechanisms need to be augmented with domain specific mechanisms to facilitate coordination. Part II is a review of design coordination from an operational perspective
MULTI AGENT-BASED ENVIRONMENTAL LANDSCAPE (MABEL) - AN ARTIFICIAL INTELLIGENCE SIMULATION MODEL: SOME EARLY ASSESSMENTS
The Multi Agent-Based Environmental Landscape model (MABEL) introduces a Distributed Artificial Intelligence (DAI) systemic methodology, to simulate land use and transformation changes over time and space. Computational agents represent abstract relations among geographic, environmental, human and socio-economic variables, with respect to land transformation pattern changes. A multi-agent environment is developed providing task-nonspecific problem-solving abilities, flexibility on achieving goals and representing existing relations observed in real-world scenarios, and goal-based efficiency. Intelligent MABEL agents acquire spatial expressions and perform specific tasks demonstrating autonomy, environmental interactions, communication and cooperation, reactivity and proactivity, reasoning and learning capabilities. Their decisions maximize both task-specific marginal utility for their actions and joint, weighted marginal utility for their time-stepping. Agent behavior is achieved by personalizing a dynamic utility-based knowledge base through sequential GIS filtering, probability-distributed weighting, joint probability Bayesian correlational weighting, and goal-based distributional properties, applied to socio-economic and behavioral criteria. First-order logics, heuristics and appropriation of time-step sequences employed, provide a simulation-able environment, capable of re-generating space-time evolution of the agents.Environmental Economics and Policy,
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