Following the recent trend of studying the theory of belief revision under the Horn fragment of propositional logic this paper develops a fully characterised Horn contraction which is analogous to the traditional tran...
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ISBN:
(纸本)9781577355120
Following the recent trend of studying the theory of belief revision under the Horn fragment of propositional logic this paper develops a fully characterised Horn contraction which is analogous to the traditional transitively relational partial meet contraction [Alchourrón et al., 1985]. This Horn contraction extends the partial meet Horn contraction studied in [Delgrande and Wassermann, 2010] so that it is guided by a transitive relation that models the ordering of plausibility over sets of beliefs.
In this paper we present a new algorithm for generatively learning the structure of Markov logic Networks. This algorithm relies on a graph of predicates, which summarizes the links existing between predicates and on ...
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ISBN:
(纸本)9781577355120
In this paper we present a new algorithm for generatively learning the structure of Markov logic Networks. This algorithm relies on a graph of predicates, which summarizes the links existing between predicates and on relational information between ground atoms in the training database. Candidate clauses are produced by means of a heuristical variabilization technique. According to our first experiments, this approach appears to be promising.
Environmental information obtained through Life Cycle Analysis techniques has been incorporated into a Mixed Integer Linear programming (MILP). The solution of the model provides the optimal configuration and operatio...
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Environmental information obtained through Life Cycle Analysis techniques has been incorporated into a Mixed Integer Linear programming (MILP). The solution of the model provides the optimal configuration and operation of an energy supply system to be installed, minimizing the environmental burden associated with production of equipment and consumption of resources. Starting from a superstructure of cogeneration system with additional components highly interconnected, the energy supply system was optimized considering specific demands of a hospital located in Zaragoza, Spain. The objective functions took into account the kilograms of CO2 released and Eco-indicator 99 Single Score. Also considered were price of energy resources, price and amortization possibilities of the equipment and options for selling surplus electricity to the electric grid. The effect of electricity generation conditions on the optimal configuration was examined by varying the source of electricity production in Spain and considering natural gas/electricity mixes from alternate countries. The ratio between local electricity emissions and natural gas emissions (alpha factor) was found to have the highest impact on the configuration of the system. Therefore the alpha factor could be considered the strongest influencing factor when deciding the optimal configuration of a system that minimizes environmental loads. (C) 2010 Elsevier Ltd. All rights reserved.
We propose a general MCMC method for Bayesian inference in logic-based probabilistic modeling. It covers a broad class of generativemodels including Bayesian networks and PCFGs. The idea is to generalize an MCMC metho...
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ISBN:
(纸本)9781577355120
We propose a general MCMC method for Bayesian inference in logic-based probabilistic modeling. It covers a broad class of generativemodels including Bayesian networks and PCFGs. The idea is to generalize an MCMC method for PCFGs to the one for a Turing-complete probabilistic modeling language PRISM in the context of statistical abduction where parse trees are replaced with explanations. We describe how to estimate the marginal probability of data from MCMC samples and how to perform Bayesian Viterbi inference using an example of Naive Bayes model augmented with a hidden variable.
We consider an extension of the propositional modal logic S4 which allows to act not only on isolated formulas, but also on sets of formulas. The interpretation of Γ is then given by the tangled closure of the valuat...
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ISBN:
(纸本)9781577355120
We consider an extension of the propositional modal logic S4 which allows to act not only on isolated formulas, but also on sets of formulas. The interpretation of Γ is then given by the tangled closure of the valuations of formulas in Γ, which over finite transitive, reflexive models indicates the existence of a cluster satisfying Γ. This extension has been shown to be more expressive than the basic modal language: for example, it is equivalent to the bisimulation-invariant fragment of FOL over finite S4 models, whereas the basic modal language is weaker. However, previous analyses of this logic have been entirely semantic, and no proof system was available. In this paper we present a sound proof system for the polyadic S4 and prove that it is complete. The axiomatization is fairly standard, adding only the fixpoint axioms of the tangled closure to the usual S4 axioms. The proof proceeds by explicitly constructing a finite model from a consistent set of formulas.
The last two decades has seen the emergence of many different probabilistic logics that use logical languages to specify, and sometimes reason, with probability distributions. Probabilistic logics that support reasoni...
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ISBN:
(纸本)9781577355120
The last two decades has seen the emergence of many different probabilistic logics that use logical languages to specify, and sometimes reason, with probability distributions. Probabilistic logics that support reasoning with probability distributions, such as ProbLog, use an implicit definition of an interaction rule to combine probabilistic evidence about atoms. In this paper, we show that this interaction rule is an example of a more general class of interactions that can be described by non-monotonic logics. We furthermore show that such local interactions about the probability of an atom can be described by convolution. The resulting extended probabilistic logic supports non-monotonic reasoning with probabilistic information.
Possibilistic logic is a well-known framework for dealing with uncertainty and reasoning under inconsistent knowledge bases. Standard possibilistic logic expressions are propositional logic formulas associated with po...
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ISBN:
(纸本)9781577355120
Possibilistic logic is a well-known framework for dealing with uncertainty and reasoning under inconsistent knowledge bases. Standard possibilistic logic expressions are propositional logic formulas associated with positive real degrees belonging to [0,1]. However, in practice it may be difficult for an expert to provide exact degrees associated with formulas of a knowledge base. This paper proposes a flexible representation of uncertain information where the weights associated with formulas are in the form of intervals. We first study a framework for reasoning with interval-based possibilistic knowledge bases by extending main concepts of possibilistic logic such as the ones of necessity and possibility measures. We then provide a characterization of an interval-based possibilistic logic base by means of a concept of compatible standard possibilistic logic bases. We show that intervalbased possibilistic logic extends possibilistic logic in the case where all intervals are singletons. Lastly, we provide computational complexity results of deriving plausible conclusions from interval-based possibilistic bases and we show that the flexibility in representing uncertain information is handled without extra computational costs.
This paper focuses on computing first-order theories under either stable model semantics or circumscription. A reduction from first-order theories to logic programs under stable model semantics over finite structures ...
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ISBN:
(纸本)9781577355120
This paper focuses on computing first-order theories under either stable model semantics or circumscription. A reduction from first-order theories to logic programs under stable model semantics over finite structures is proposed, and an embedding of circumscription into stable model semantics is also given. Having such reduction and embedding, reasoning problems represented by first-order theories under these two semantics can then be handled by using existing answer set solvers. The effectiveness of this approach in computing hard problems beyond NP is demonstrated by some experiments.
Topic models have been used successfully for a variety of problems, often in the form of application-specific extensions of the basic Latent Dirichlet Allocation (LDA) model. Because deriving these new models in order...
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ISBN:
(纸本)9781577355120
Topic models have been used successfully for a variety of problems, often in the form of application-specific extensions of the basic Latent Dirichlet Allocation (LDA) model. Because deriving these new models in order to encode domain knowledge can be difficult and time-consuming, we propose the Fold-all model, which allows the user to specify general domain knowledge in First-Order logic (FOL). However, combining topic modeling with FOL can result in inference problems beyond the capabilities of existing techniques. We have therefore developed a scalable inference technique using stochastic gradient descent which may also be useful to the Markov logic Network (MLN) research community. Experiments demonstrate the expressive power of Fold-all, as well as the scalability of our proposed inference method.
Constrained partially observable Markov decision processes (CPOMDPs) extend the standard POMDPs by allowing the specification of constraints on some aspects of the policy in addition to the optimality objective for th...
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ISBN:
(纸本)9781577355120
Constrained partially observable Markov decision processes (CPOMDPs) extend the standard POMDPs by allowing the specification of constraints on some aspects of the policy in addition to the optimality objective for the value function. CPOMDPs have many practical advantages over standard POMDPs since they naturally model problems involving limited resource or multiple objectives. In this paper, we show that the optimal policies in CPOMDPs can be randomized, and present exact and approximate dynamic programming methods for computing randomized optimal policies. While the exact method requires solving a minimax quadratically constrained program (QCP) in each dynamic programming update, the approximate method utilizes the point-based value update with a linear program (LP). We show that the randomized policies are significantly better than the deterministic ones. We also demonstrate that the approximate point-based method is scalable to solve large problems.
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