Negative Reinforcement and Backtrack-Points for Recurrent Neural Networks for Cost-Based Abduction

Abduction is the process of proceeding from data describing a set of observations or events, to a set of hypotheses which best explains or accounts for the data. Cost-based abduction (CKA) is an AI formalism in which evidence to be explained is treated as a goal to be proven, proofs have costs based on how much needs to be assumed to complete the proof, and the set of assumptions needed to complete the least-cost proof are taken as the best explanation for the given evidence. In this paper, we introduce two techniques for improving the performance of high order recurrent networks (HORN) applied to cost-based abduction. In the backtrack-points technique, we use heuristics to recognize early that the network trajectory is moving in the wrong direction; we then restore the network state to a previously-stored point, and apply heuristic perturbations to nudge the network trajectory in a different direction. In the negative reinforcement technique, we add hyperedges to the network to reduce the attractiveness of local-minima. We apply these techniques on a 300-hypothesis, 900-rule particularly-difficult instance of CBA

Parallel versus iterated: comparing population oriented and chained sequential simulated annealing approaches to cost-based abduction

Stochastic search techniques are used to solve NP-hard combinatorial optimization problems. Simulated annealing, genetic algorithms and hybridization of both, all attempt to find the best solution with minimal cost and time. Guided Evolutionary Simulated Annealing is one technique of such hybridization. It is based on evolutionary programming where a number of simulated annealing chains are working in a generation to find the optimum solution for a problem. Abduction is the problem of finding the best explanation to a given set of observations. In AI, this has been modeled by a set of hypotheses that need to be assumed to prove the observation or goal. Cost-Based Abduction (CBA) associates a cost to each hypothesis. It is an example of an NP-hard problem, where the objective is to minimize the cost of the assumed hypotheses to prove the goal. Analyzing the search space of a problem is one way of understanding its nature and categorizing it into straightforward, misleading or difficult for genetic algorithms. Fitness-Distance Correlation and Fitness-Distance plots are helpful tools in such analysis. This thesis examines solving the CBA problem using Simulated Annealing and Guided Evolutionary Simulated Annealing and analyses the Fitness-Distance landscape of some Cost-Based abduction problem instances

Optimal weekly releases from a multireservoir hydropower system

The operation of a multi-unit electric energy generating system is studied under certain and uncertain future inflow conditions. The generating units include thermoplants, hydroplants with regulating reservoir and run-of-river hydroplants. The objective is to minimize the expected cost of the operation of the system while meeting a previously defined energy demand. A case study is formulated based on the electric energy generating system of the South of Brazil. The system is composed of 6 hydroplants with regulating reservoirs, 2 run-of-river hydroplants, and 8 thermoplants. In order to obtain a better insight into the nature and peculiarities of the system's operation it is initially studied considering the future to be deterministic. An aggregation-optimization-disaggregation procedure is proposed to identify a near optimal solution while reducing substantially the computational effort. This consists of the development of an aggregated representation of the system composed of a hypothetical and unique reservoir with overall energy inflows and releases. Optimal operation of the aggregated system is determined by a new and efficient optimization technique specifically developed for this problem. A disaggregation procedure defines the operation of each system's unit given the operation of the aggregated system. The procedure is based on a heuristic approach that has as a main objective to minimize water spills. An aggregated representation of the system is again adopted for the definition of optimal strategy of operation when the future inflows are uncertain. The characteristics of operation of each reservoir are introduced into the aggregated formulation utilizing the peculiarities of the optimal deterministic operation. A modification of Massé's Chain of Marginal Expectations is used in the computations. The resultant strategy of operation can be presented as a function of aggregated values of energy storage and inflow. The strategy explicitly considers the autocorrelation of aggregated energy inflows. The strategy also implicitly accounts for the cross-correlations among the energy storages and inflows to each reservoir. Finally, a substantial part of the autocorrelation of the energy inflows and storages in each reservoir is indirectly considered in the strategy. Theoretical significance of the strategy is obtained without burdensome computational effort

Models and methods for the modelling of forest managing

A gestão florestal é uma actividade de grande valor económico e importância ecológica. As áreas florestais geridas podem abranger regiões muito grandes e a sua gestão adequada é muito importante para um desenvolvimento eficaz, tanto em termos de planeamento económico como de recursos naturais, e gerir uma floresta implica tipicamente a aplicação de escolhas políticas em diferentes parcelas de terra, aqui referidas como Stands ou Management Units (Unidades de Gestão). Este documento analisa vários métodos de gestão florestal, juntamente com as suas variações disponíveis na literatura ao longo de 4 capítulos, sendo esses métodos o Unit Restriction Model and Area Restriction Model por Alan T. Murray, the Area Restriction Model with Stand-Clear-Cut variables por Constantino et al, the Path Algorithm and the Generalized Management Unit formulation por McDill et al and the Full Adjacent Unit formulation por Gharbi et al. Os resultados apresentados nos artigos originais são discutidos nas conclusões de cada capítulo. Os 2 últimos capítulos apresentam uma formulação de Constraint Programming do problema e a sua implementação utilizando a biblioteca Choco da linguagem de programação Java e apresentam também os resultados, um capítulo relacionado com a primeira implementação que trata apenas da optimização do Madeira Total Obtida e o outro alargando o problema, juntamente com um novo conjunto de dados, para lidar com a optimização multicritério. Para o fazer, os princípios de Constraint Programming são primeiro enumerados juntamente com uma breve história da tecnologia de Constraint Programming. Finalmente, outros possíveis desenvolvimentos são discutidos numa secção de Trabalho Futuro; Abstract: Forest management is an activity of prime economic and ecological importance. Managed forest areas can span very large regions and their proper management is paramount to an effective development, in terms both of economic and natural resources planning and managing a forest typically implies applying policy choices to different patches of land, here referred to as Stands or Management Units. This paper reviews several methods of forest management alongside their variations available in the literature throughout 4 chapters, those methods being the Unit Restriction Model and Area Restriction Model by Alan T. Murray, the Area Restriction Model with Stand-Clear-Cut variables by Constantino et al, the Path Algorithm and the Generalized Management Unit formulation by McDill et al and the Full Adjacent Unit formulation by Gharbi et al. The results as presented in the original papers are discussed in the conclusions of each chapter. The final 2 chapters present a Constraint Programming formulation of the problem and its implementation using the Choco framework of the Java programming language and showcases the results, one chapter relating to the first implementation that deals only with the optimization of total Wood Yield and the other broadening the problem, alongside a new dataset, to deal with multi-criteria optimization. In order to do this the principles of Constraint Programming are first enumerated along with a short history of Constraint Programming technology

EXEIS, Expert screening and optimal extraction/injection pumping systems for short-term plume immobilization

This report presents the EXEIS family of micro-computer based programs for achieving short-term contaminant plume containment. EXEIS is applicable if contaminated water cannot be extracted and water cannot be imported to or exported from the site. There are two main purposes and types of users. For persons relatively unfamiliar with groundwater remedial actions, an expert screening system gives guidance concerning whether extraction/injection (Eli) pumping, slurry wall or sheet piling are most appropriate. For personal more experienced in remedial actions, management models compute optimal E/I strategies for short-term containment. Via deterministic and stochastic multiobjective optimization models, uncertainty in both planning horizon and aquifer parameters is addressed

An Integer Programming approach to Bayesian Network Structure Learning

We study the problem of learning a Bayesian Network structure from data using an Integer Programming approach. We study the existing approaches, an in particular some recent works that formulate the problem as an Integer Programming model. By discussing some weaknesses of the existing approaches, we propose an alternative solution, based on a statistical sparsification of the search space. Results show how our approach can lead to promising results, especially for large network