27,360 research outputs found

    Rational physical agent reasoning beyond logic

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    The paper addresses the problem of defining a theoretical physical agent framework that satisfies practical requirements of programmability by non-programmer engineers and at the same time permitting fast realtime operation of agents on digital computer networks. The objective of the new framework is to enable the satisfaction of performance requirements on autonomous vehicles and robots in space exploration, deep underwater exploration, defense reconnaissance, automated manufacturing and household automation

    The 1990 progress report and future plans

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    This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers

    Why (and How) Networks Should Run Themselves

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    The proliferation of networked devices, systems, and applications that we depend on every day makes managing networks more important than ever. The increasing security, availability, and performance demands of these applications suggest that these increasingly difficult network management problems be solved in real time, across a complex web of interacting protocols and systems. Alas, just as the importance of network management has increased, the network has grown so complex that it is seemingly unmanageable. In this new era, network management requires a fundamentally new approach. Instead of optimizations based on closed-form analysis of individual protocols, network operators need data-driven, machine-learning-based models of end-to-end and application performance based on high-level policy goals and a holistic view of the underlying components. Instead of anomaly detection algorithms that operate on offline analysis of network traces, operators need classification and detection algorithms that can make real-time, closed-loop decisions. Networks should learn to drive themselves. This paper explores this concept, discussing how we might attain this ambitious goal by more closely coupling measurement with real-time control and by relying on learning for inference and prediction about a networked application or system, as opposed to closed-form analysis of individual protocols

    Developing computational thinking in the classroom: a framework

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    Computational thinking sits at the heart of the new statutory programme of study for Computing: “A high quality computing education equips pupils to use computational thinking and creativity to understand and change the world” (Department for Education, 2013, p. 188). This document aims to support teachers to teach computational thinking. It describes a framework that helps explain what computational thinking is, describes pedagogic approaches for teaching it and gives ways to assess it. Pupil progression with the previous ICT curriculum was often demonstrated through ‘how’ (for example, a software usage skill) or ‘what’ the pupil produced (for example, a poster). This was partly due to the needs of the business world for office skills. Such use of precious curriculum time however has several weaknesses. Firstly, the country’s economy depends on technological innovation not just on use of technology. Secondly, the pace of technology and organisational change is fast in that the ICT skills learnt are out of date before a pupil leaves school. Thirdly, technology invades all aspects of our life and the typically taught office practice is only a small part of technology use today

    Crossmodal content binding in information-processing architectures

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    Operating in a physical context, an intelligent robot faces two fundamental problems. First, it needs to combine information from its different sensors to form a representation of the environment that is more complete than any of its sensors on its own could provide. Second, it needs to combine high-level representations (such as those for planning and dialogue) with its sensory information, to ensure that the interpretations of these symbolic representations are grounded in the situated context. Previous approaches to this problem have used techniques such as (low-level) information fusion, ontological reasoning, and (high-level) concept learning. This paper presents a framework in which these, and other approaches, can be combined to form a shared representation of the current state of the robot in relation to its environment and other agents. Preliminary results from an implemented system are presented to illustrate how the framework supports behaviours commonly required of an intelligent robot
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