86,157 research outputs found

    Discussing the common(s) in neoliberal Capitalism: from Ontology to Politics

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    This article intends to think the relationship between neoliberal capitalism and the common(s). First, it ties to define the common both in ontological and political terms, stressing the similarities and differences between the common, commons and common goods. Then, it characterizes their relationship with neoliberal capitalism in terms of dispossession, expropriation and configuration. Finally, it discusses if the common can be thought as an alternative form of cooperation and self-government with regards to neoliberalism and to what extent it can be posed as a complete alternative to the state

    Synthesized cooperative strategies for intelligent multi-robots in a real-time distributed environment : a thesis presented in partial fulfillment of the requirements for the degree of Master of Science in Computer Science at Massey University, Albany, New Zealand

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    In the robot soccer domain, real-time response usually curtails the development of more complex Al-based game strategies, path-planning and team cooperation between intelligent agents. In light of this problem, distributing computationally intensive algorithms between several machines to control, coordinate and dynamically assign roles to a team of robots, and allowing them to communicate via a network gives rise to real-time cooperation in a multi-robotic team. This research presents a myriad of algorithms tested on a distributed system platform that allows for cooperating multi- agents in a dynamic environment. The test bed is an extension of a popular robot simulation system in the public domain developed at Carnegie Mellon University, known as TeamBots. A low-level real-time network game protocol using TCP/IP and UDP were incorporated to allow for a conglomeration of multi-agent to communicate and work cohesively as a team. Intelligent agents were defined to take on roles such as game coach agent, vision agent, and soccer player agents. Further, team cooperation is demonstrated by integrating a real-time fuzzy logic-based ball-passing algorithm and a fuzzy logic algorithm for path planning. Keywords Artificial Intelligence, Ball Passing, the coaching system, Collaborative, Distributed Multi-Agent, Fuzzy Logic, Role Assignmen

    Collective navigation of complex networks: Participatory greedy routing

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    Many networks are used to transfer information or goods, in other words, they are navigated. The larger the network, the more difficult it is to navigate efficiently. Indeed, information routing in the Internet faces serious scalability problems due to its rapid growth, recently accelerated by the rise of the Internet of Things. Large networks like the Internet can be navigated efficiently if nodes, or agents, actively forward information based on hidden maps underlying these systems. However, in reality most agents will deny to forward messages, which has a cost, and navigation is impossible. Can we design appropriate incentives that lead to participation and global navigability? Here, we present an evolutionary game where agents share the value generated by successful delivery of information or goods. We show that global navigability can emerge, but its complete breakdown is possible as well. Furthermore, we show that the system tends to self-organize into local clusters of agents who participate in the navigation. This organizational principle can be exploited to favor the emergence of global navigability in the system.Comment: Supplementary Information and Videos: https://koljakleineberg.wordpress.com/2016/11/14/collective-navigation-of-complex-networks-participatory-greedy-routing

    The Contribution of Society to the Construction of Individual Intelligence

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    It is argued that society is a crucial factor in the construction of individual intelligence. In other words that it is important that intelligence is socially situated in an analogous way to the physical situation of robots. Evidence that this may be the case is taken from developmental linguistics, the social intelligence hypothesis, the complexity of society, the need for self-reflection and autism. The consequences for the development of artificial social agents is briefly considered. Finally some challenges for research into socially situated intelligence are highlighted

    Generating and Adapting to Diverse Ad-Hoc Cooperation Agents in Hanabi

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    Hanabi is a cooperative game that brings the problem of modeling other players to the forefront. In this game, coordinated groups of players can leverage pre-established conventions to great effect, but playing in an ad-hoc setting requires agents to adapt to its partner's strategies with no previous coordination. Evaluating an agent in this setting requires a diverse population of potential partners, but so far, the behavioral diversity of agents has not been considered in a systematic way. This paper proposes Quality Diversity algorithms as a promising class of algorithms to generate diverse populations for this purpose, and generates a population of diverse Hanabi agents using MAP-Elites. We also postulate that agents can benefit from a diverse population during training and implement a simple "meta-strategy" for adapting to an agent's perceived behavioral niche. We show this meta-strategy can work better than generalist strategies even outside the population it was trained with if its partner's behavioral niche can be correctly inferred, but in practice a partner's behavior depends and interferes with the meta-agent's own behavior, suggesting an avenue for future research in characterizing another agent's behavior during gameplay.Comment: arXiv admin note: text overlap with arXiv:1907.0384

    10-02 "Care Ethics and Markets: A View from Feminist Economics"

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    It is common to think of care ethics and justice ethics as being opposed to each other, and also to think of economic life as being opposed to social life. As a result, it may be hard to see how care ethics, seen as interpersonal, could be applicable to business, when the latter is perceived as asocial. This essay uncovers the origins of these beliefs in unhelpful dualistic cognitive habits and in gender-biases in the development of the discipline of economics. In particular, feminist analysis reveals the mythical nature of both "economic man" and the belief in mechanical "profit maximization." The essay calls for unveiling and recognizing the ethical and connected dimensions that already characterize business life, and including these in thinking about how to create a more humane economy.

    An agent-based framework for selection of partners in dynamic virtual enterprises

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    Advances in computer networking technology and open system standards have made practically feasible to create and manage virtual enterprises. A virtual enterprise, VE, is usually defined as a temporary alliance of enterprises that come together to share their skills, core competencies, and resources in order to better respond to business opportunities, and whose cooperation is supported by computer networks. The materialization of this paradigm, although enabled by recent advances in communication technologies, computer networks and logistics, requires an appropriate architectural framework and support tools. In this paper we propose an agent-based model of a dynamic VE to support the different selection processes that are used in selecting the partners for a dynamic VE, where the partners of a VE are represented by agents. Such a framework will form the basis for tools that provide automated support for creation, and operation, of dynamic virtual enterprises

    Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence

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    Learning agents that are not only capable of taking tests, but also innovating is becoming a hot topic in AI. One of the most promising paths towards this vision is multi-agent learning, where agents act as the environment for each other, and improving each agent means proposing new problems for others. However, existing evaluation platforms are either not compatible with multi-agent settings, or limited to a specific game. That is, there is not yet a general evaluation platform for research on multi-agent intelligence. To this end, we introduce Arena, a general evaluation platform for multi-agent intelligence with 35 games of diverse logics and representations. Furthermore, multi-agent intelligence is still at the stage where many problems remain unexplored. Therefore, we provide a building toolkit for researchers to easily invent and build novel multi-agent problems from the provided game set based on a GUI-configurable social tree and five basic multi-agent reward schemes. Finally, we provide Python implementations of five state-of-the-art deep multi-agent reinforcement learning baselines. Along with the baseline implementations, we release a set of 100 best agents/teams that we can train with different training schemes for each game, as the base for evaluating agents with population performance. As such, the research community can perform comparisons under a stable and uniform standard. All the implementations and accompanied tutorials have been open-sourced for the community at https://sites.google.com/view/arena-unity/
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