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Personalized explanation in machine learning
Explanation in machine learning and related fields such as artificial in...
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Generating Decision Structures and Causal Explanations for Decision Making
This paper examines two related problems that are central to developing ...
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Using machine learning to make constraint solver implementation decisions
Programs to solve so-called constraint problems are complex pieces of so...
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How to Manipulate CNNs to Make Them Lie: the GradCAM Case
Recently many methods have been introduced to explain CNN decisions. How...
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'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions
Data-driven decision-making consequential to individuals raises importan...
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Neuroscientific User Models: The Source of Uncertain User Feedback and Potentials for Improving Web Personalisation
In this paper we consider the neuroscientific theory of the Bayesian bra...
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Explanation of Probabilistic Inference for Decision Support Systems
An automated explanation facility for Bayesian conditioning aimed at imp...
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Theory-Based Inductive Learning: An Integration of Symbolic and Quantitative Methods
The objective of this paper is to propose a method that will generate a causal explanation of observed events in an uncertain world and then make decisions based on that explanation. Feedback can cause the explanation and decisions to be modified. I call the method Theory-Based Inductive Learning (T-BIL). T-BIL integrates deductive learning, based on a technique called Explanation-Based Generalization (EBG) from the field of machine learning, with inductive learning methods from Bayesian decision theory. T-BIL takes as inputs (1) a decision problem involving a sequence of related decisions over time, (2) a training example of a solution to the decision problem in one period, and (3) the domain theory relevant to the decision problem. T-BIL uses these inputs to construct a probabilistic explanation of why the training example is an instance of a solution to one stage of the sequential decision problem. This explanation is then generalized to cover a more general class of instances and is used as the basis for making the next-stage decisions. As the outcomes of each decision are observed, the explanation is revised, which in turn affects the subsequent decisions. A detailed example is presented that uses T-BIL to solve a very general stochastic adaptive control problem for an autonomous mobile robot.
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