Reservoir water release dynamic decision model based on spatial temporal pattern

The multi-purpose reservoir water release decision requires an expert to make a decision by assembling complex decision information that occurred in real time. The decision needs to consider adequate reservoir water balance in order to maintain reservoir multi-purpose function and provide enough space for incoming heavy rainfall and inflow. Crucially, the water release should not exceed the downstream maximum river level so that it will not cause flood. The rainfall and water level are fuzzy information, thus the decision model needs the ability to handle the fuzzy information. Moreover, the rainfalls that are recorded at different location take different time to reach into the reservoir. This situation shows that there is spatial temporal relationship hidden in between each gauging station and the reservoir. Thus, this study proposed dynamic reservoir water release decision model that utilize both spatial and temporal information in the input pattern. Based on the patterns, the model will suggest when the reservoir water should be released. The model adopts Adaptive Neuro-Fuzzy Inference System (ANFIS) in order to deal with the fuzzy information. The data used in this study was obtained from the Perlis Department of Irrigation and Drainage. The modified Sliding Window algorithm was used to construct the rainfall temporal pattern, while the spatial information was established by simulating the mapped rainfall and reservoir water level pattern. The model performance was measured based on the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Findings from this study shows that ANFIS produces the lowest RMSE and MAE when compare to Autoregressive Integrated Moving Average (ARIMA) and Backpropagation Neural Network (BPNN) model. The model can be used by the reservoir operator to assist their decision making and support the new reservoir operator in the absence of an experience reservoir operator

Systematic literature review of validation methods for AI systems

Context: Artificial intelligence (AI) has made its way into everyday activities, particularly through new techniques such as machine learning (ML). These techniques are implementable with little domain knowledge. This, combined with the difficulty of testing AI systems with traditional methods, has made system trustworthiness a pressing issue. Objective: This paper studies the methods used to validate practical AI systems reported in the literature. Our goal is to classify and describe the methods that are used in realistic settings to ensure the dependability of AI systems. Method: A systematic literature review resulted in 90 papers. Systems presented in the papers were analysed based on their domain, task, complexity, and applied validation methods. Results: The validation methods were synthesized into a taxonomy consisting of trial, simulation, model-centred validation, and expert opinion. Failure monitors, safety channels, redundancy, voting, and input and output restrictions are methods used to continuously validate the systems after deployment. Conclusions: Our results clarify existing strategies applied to validation. They form a basis for the synthesization, assessment, and refinement of AI system validation in research and guidelines for validating individual systems in practice. While various validation strategies have all been relatively widely applied, only few studies report on continuous validation.Peer reviewe

Acceptance in Incomplete Argumentation Frameworks

A Abstract argumentation frameworks (AFs), originally proposed by Dung, constitute a central formal model for the study of computational aspects of argumentation in AI. Credulous and skeptical acceptance of arguments in a given AF are well-studied problems both in terms of theoretical analysis-especially computational complexity-and the development of practical decision procedures for the problems. However, AFs make the assumption that all attacks between arguments are certain (i.e., present attacks are known to exist, and missing attacks are known to not exist), which can in various settings be a restrictive assumption. A generalization of AFs to incomplete AFs was recently proposed as a formalism that allows the representation of both uncertain attacks and uncertain arguments in AFs. In this article, we explore the impact of allowing for modeling such uncertainties in AFs on the computational complexity of natural generalizations of acceptance problems to incomplete AFs under various central AF semantics. Complementing the complexity-theoretic analysis, we also develop the first practical decision procedures for all of the NP-hard variants of acceptance in incomplete AFs. In terms of complexity analysis, we establish a full complexity landscape, showing that depending on the variant of acceptance and property/semantics, the complexity of acceptance in incomplete AFs ranges from polynomial-time decidable to completeness for Sigma(p)(3). In terms of algorithms, we show through an extensive empirical evaluation that an implementation of the proposed decision procedures, based on boolean satisfiability (SAT) solving, is effective in deciding variants of acceptance under uncertainties. We also establish conditions for what type of atomic changes are guaranteed to be redundant from the perspective of preserving extensions of completions of incomplete AFs, and show that the results allow for considerably improving the empirical efficiency of the proposed SAT-based counterexample-guided abstraction refinement algorithms for acceptance in incomplete AFs for problem variants with complexity beyond NP. (C) 2021 The Authors. Published by Elsevier B.V.Peer reviewe