3,172 research outputs found

    Symmetries in planning problems

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    Symmetries arise in planning in a variety of ways. This paper describes the ways that symmetry aises most naturally in planning problems and reviews the approaches that have been applied to exploitation of symmetry in order to reduce search for plans. It then introduces some extensions to the use of symmetry in planning before moving on to consider how the exploitation of symmetry in planning might be generalised to offer new approaches to exploitation of symmetry in other combinatorial search problems

    Transverse-field Ising spin chain with inhomogeneous disorder

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    We consider the critical and off-critical properties at the boundary of the random transverse-field Ising spin chain when the distribution of the couplings and/or transverse fields, at a distance ll from the surface, deviates from its uniform bulk value by terms of order lκl^{-\kappa} with an amplitude AA. Exact results are obtained using a correspondence between the surface magnetization of the model and the surviving probability of a random walk with time-dependent absorbing boundary conditions. For slow enough decay, κ<1/2\kappa<1/2, the inhomogeneity is relevant: Either the surface stays ordered at the bulk critical point or the average surface magnetization displays an essential singularity, depending on the sign of AA. In the marginal situation, κ=1/2\kappa=1/2, the average surface magnetization decays as a power law with a continuously varying, AA-dependent, critical exponent which is obtained analytically. The behavior of the critical and off-critical autocorrelation functions as well as the scaling form of the probability distributions for the surface magnetization and the first gaps are determined through a phenomenological scaling theory. In the Griffiths phase, the properties of the Griffiths-McCoy singularities are not affected by the inhomogeneity. The various results are checked using numerical methods based on a mapping to free fermions.Comment: 11 pages (Revtex), 11 figure

    In defense of compilation: A response to Davis' form and content in model-based reasoning

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    In a recent paper entitled 'Form and Content in Model Based Reasoning', Randy Davis argues that model based reasoning research aimed at compiling task specific rules from underlying device models is mislabeled, misguided, and diversionary. Some of Davis' claims are examined and his basic conclusions are challenged about the value of compilation research to the model based reasoning community. In particular, Davis' claim is refuted that model based reasoning is exempt from the efficiency benefits provided by knowledge compilation techniques. In addition, several misconceptions are clarified about the role of representational form in compilation. It is concluded that techniques have the potential to make a substantial contribution to solving tractability problems in model based reasoning

    Discovering logical knowledge in non-symbolic domains

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    Deep learning and symbolic artificial intelligence remain the two main paradigms in Artificial Intelligence (AI), each presenting their own strengths and weaknesses. Artificial agents should integrate both of these aspects of AI in order to show general intelligence and solve complex problems in real-world scenarios; similarly to how humans use both the analytical left side and the intuitive right side of their brain in their lives. However, one of the main obstacles hindering this integration is the Symbol Grounding Problem [144], which is the capacity to map physical world observations to a set of symbols. In this thesis, we combine symbolic reasoning and deep learning in order to better represent and reason with abstract knowledge. In particular, we focus on solving non-symbolic-state Reinforcement Learning environments using a symbolic logical domain. We consider different configurations: (i) unknown knowledge of both the symbol grounding function and the symbolic logical domain, (ii) unknown knowledge of the symbol grounding function and prior knowledge of the domain, (iii) imperfect knowledge of the symbols grounding function and unknown knowledge of the domain. We develop algorithms and neural network architectures that are general enough to be applied to different kinds of environments, which we test on both continuous-state control problems and image-based environments. Specifically, we develop two kinds of architectures: one for Markovian RL tasks and one for non-Markovian RL domains. The first is based on model-based RL and representation learning, and is inspired by the substantial prior work in state abstraction for RL [115]. The second is mainly based on recurrent neural networks and continuous relaxations of temporal logic domains. In particular, the first approach extracts a symbolic STRIPS-like abstraction for control problems. For the second approach, we explore connections between recurrent neural networks and finite state machines, and we define Visual Reward Machines, an extension to non-symbolic domains of Reward Machines [27], which are a popular approach to non-Markovian RL tasks

    Proceedings of the Workshop on Change of Representation and Problem Reformulation

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    The proceedings of the third Workshop on Change of representation and Problem Reformulation is presented. In contrast to the first two workshops, this workshop was focused on analytic or knowledge-based approaches, as opposed to statistical or empirical approaches called 'constructive induction'. The organizing committee believes that there is a potential for combining analytic and inductive approaches at a future date. However, it became apparent at the previous two workshops that the communities pursuing these different approaches are currently interested in largely non-overlapping issues. The constructive induction community has been holding its own workshops, principally in conjunction with the machine learning conference. While this workshop is more focused on analytic approaches, the organizing committee has made an effort to include more application domains. We have greatly expanded from the origins in the machine learning community. Participants in this workshop come from the full spectrum of AI application domains including planning, qualitative physics, software engineering, knowledge representation, and machine learning

    The Stanford how things work project

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    We provide an overview of the Stanford How Things Work (HTW) project, an ongoing integrated collection of research activities in the Knowledge Systems Laboratory at Stanford University. The project is developing technology for representing knowledge about engineered devices in a form that enables the knowledge to be used in multiple systems for multiple reasoning tasks and reasoning methods that enable the represented knowledge to be effectively applied to the performance of the core engineering task of simulating and analyzing device behavior. The central new capabilities currently being developed in the project are automated assistance with model formulation and with verification that a design for an electro-mechanical device satisfies its functional specification

    How to sustain entrepreneurial performance during the current financial crisis

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    In a debt-ridden society that badly needs to grow economically, policies controlling the flows of economic accounts (revenues and expenditures) should be consistent with an efficient “asset and liability management”. The extra money obtained from immediate sales of idle or low-productive government properties can boost economic growth if lent to innovative entrepreneurial firms

    PROFESSIONAL DIVERSITY IN AGRICULTURAL ECONOMICS: SALVATION OR SUICIDE?

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    Teaching/Communication/Extension/Profession,
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