232,435 research outputs found

    Bridging Symbolic and Sub-Symbolic AI: Towards Cooperative Transfer Learning in Multi-Agent Systems

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    Cooperation and knowledge sharing are of paramount importance in the evolution of an intelligent species. Knowledge sharing requires a set of symbols with a shared interpretation, enabling effective communication supporting cooperation. The engineering of intelligent systems may then benefit from the distribution of knowledge among multiple components capable of cooperation and symbolic knowledge sharing. Accordingly, in this paper, we propose a roadmap for the exploitation of knowledge representation and sharing to foster higher degrees of artificial intelligence. We do so by envisioning intelligent systems as composed by multiple agents, capable of cooperative (transfer) learning—Co(T)L for short. In CoL, agents can improve their local (sub-symbolic) knowledge by exchanging (symbolic) information among each others. In CoTL, agents can also learn new tasks autonomously by sharing information about similar tasks. Along this line, we motivate the introduction of Co(T)L and discuss benefits and feasibility

    Formal models in web based contracting

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    Legal principles have some difficulty to deal with software agents celebrating contracts and operating in e-commerce environments without direct human intervention. Autonomous intelligent agents have a control on their own actions and states, supporting or taking effective decisions. Therefore, some qualitative parameters such as trust, reputation and quality of information have to be taken under consideration to evaluate, certify and justify such decisions. Indeed, this paper shows how to construct a dynamic virtual world of complex and interacting entities or agents, organized in terms of Multi-Agent Systems (MAS), that compete against one another in order to solve a particular problem, according to a rigorous selection regime in which its fitness is judged by one criterion alone, a measure of the quality of information of the agent or agents, here understood as evolutionary logic theories. This virtual world could witness the emergence of our first learning, thinking machines, that may cater for some issues on the evolution of formal models of the world in general, and on what is concerned with the objectives set to this work, in contracting, and foray into a vast, untapped technological market.Fundação para a CiĂȘncia e a Tecnologia (FCT) - Intelligent Agents and Legal Relations Project – POCTI/ JUR/57221/2004

    Managing evolution and change in web-based teaching and learning environments

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    The state of the art in information technology and educational technologies is evolving constantly. Courses taught are subject to constant change from organisational and subject-specific reasons. Evolution and change affect educators and developers of computer-based teaching and learning environments alike – both often being unprepared to respond effectively. A large number of educational systems are designed and developed without change and evolution in mind. We will present our approach to the design and maintenance of these systems in rapidly evolving environments and illustrate the consequences of evolution and change for these systems and for the educators and developers responsible for their implementation and deployment. We discuss various factors of change, illustrated by a Web-based virtual course, with the objective of raising an awareness of this issue of evolution and change in computer-supported teaching and learning environments. This discussion leads towards the establishment of a development and management framework for teaching and learning systems

    Evolution of a supply chain management game for the trading agent competition

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    TAC SCM is a supply chain management game for the Trading Agent Competition (TAC). The purpose of TAC is to spur high quality research into realistic trading agent problems. We discuss TAC and TAC SCM: game and competition design, scientific impact, and lessons learnt

    Opinion Polarization by Learning from Social Feedback

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    We explore a new mechanism to explain polarization phenomena in opinion dynamics in which agents evaluate alternative views on the basis of the social feedback obtained on expressing them. High support of the favored opinion in the social environment, is treated as a positive feedback which reinforces the value associated to this opinion. In connected networks of sufficiently high modularity, different groups of agents can form strong convictions of competing opinions. Linking the social feedback process to standard equilibrium concepts we analytically characterize sufficient conditions for the stability of bi-polarization. While previous models have emphasized the polarization effects of deliberative argument-based communication, our model highlights an affective experience-based route to polarization, without assumptions about negative influence or bounded confidence.Comment: Presented at the Social Simulation Conference (Dublin 2017

    Conception of the cognitive engineering design problem

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    Cognitive design, as the design of cognitive work and cognitive tools, is predominantly a craft practice that currently depends on the experience and insight of the designer. However, the emergence of a discipline of cognitive engineering promises a more effective alternative practice, one that turns on the prescription of solutions to cognitive design problems. In this paper, the authors first examine the requirements for advancing cognitive engineering as a discipline. In particular, they identify the need for a conception for explicitly formulating cognitive design problems. A proposal for such a conception is then presented
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