Approaches to abductive reasoning : an overview

Abduction is a form of non-monotonic reasoning that has gained increasing interest in the last few years. The key idea behind it can be represented by the following inference rule frac{varphirightarrowomega,}{varphi}omega, i.e., from an occurrence of omega and the rule "varphi implies omega';, infer an occurrence of varphi as a plausible hypothesis or explanation for omega. Thus, in contrast to deduction, abduction is as well as induction a form of "defeasible'; inference, i.e., the formulae sanctioned are plausible and submitted to verification. In this paper, a formal description of current approaches is given. The underlying reasoning process is treated independently and divided into two parts. This includes a description of methods for hypotheses generation and methods for finding the best explanations among a set of possible ones. Furthermore, the complexity of the abductive task is surveyed in connection with its relationship to default reasoning. We conclude with the presentation of applications of the discussed approaches focusing on plan recognition and plan generation

EFFECT OF COGNITIVE BIASES ON HUMAN UNDERSTANDING OF RULE-BASED MACHINE LEARNING MODELS

PhDThis thesis investigates to what extent do cognitive biases a ect human understanding of interpretable machine learning models, in particular of rules discovered from data. Twenty cognitive biases (illusions, e ects) are analysed in detail, including identi cation of possibly e ective debiasing techniques that can be adopted by designers of machine learning algorithms and software. This qualitative research is complemented by multiple experiments aimed to verify, whether, and to what extent, do selected cognitive biases in uence human understanding of actual rule learning results. Two experiments were performed, one focused on eliciting plausibility judgments for pairs of inductively learned rules, second experiment involved replication of the Linda experiment with crowdsourcing and two of its modi cations. Altogether nearly 3.000 human judgments were collected. We obtained empirical evidence for the insensitivity to sample size e ect. There is also limited evidence for the disjunction fallacy, misunderstanding of and , weak evidence e ect and availability heuristic. While there seems no universal approach for eliminating all the identi ed cognitive biases, it follows from our analysis that the e ect of many biases can be ameliorated by making rule-based models more concise. To this end, in the second part of thesis we propose a novel machine learning framework which postprocesses rules on the output of the seminal association rule classi cation algorithm CBA [Liu et al, 1998]. The framework uses original undiscretized numerical attributes to optimize the discovered association rules, re ning the boundaries of literals in the antecedent of the rules produced by CBA. Some rules as well as literals from the rules can consequently be removed, which makes the resulting classi er smaller. Benchmark of our approach on 22 UCI datasets shows average 53% decrease in the total size of the model as measured by the total number of conditions in all rules. Model accuracy remains on the same level as for CBA

Statistical model of human lexical category disambiguation

Research in Sentence Processing is concerned with discovering the mechanism by which linguistic utterances are mapped onto meaningful representations within the human mind. Models of the Human Sentence Processing Mechanism (HSPM) can be divided into those in which such mapping is performed by a number of limited modular processes and those in which there is a single interactive process. A further, and increasingly important, distinction is between models which rely on innate preferences to guide decision processes and those which make use of experiencebased statistics. In this context, the aims of the current thesis are two-fold: • To argue that the correct architecture of the HSPM is both modular and statistical - the Modular Statistical Hypothesis (MSH). • To propose and provide empirical support for a position in which human lexical category disambiguation occurs within a modular process, distinct from syntactic parsing and guided by a statistical decision process. Arguments are given for why a modular statistical architecture should be preferred on both methodological and rational grounds. We then turn to the (often ignored) problem of lexical category disambiguation and propose the existence of a presyntactic Statistical Lexical Category Module (SLCM). A number of variants of the SLCM are introduced. By empirically investigating this particular architecture we also hope to provide support for the more general hypothesis - the MSH. The SLCM has some interesting behavioural properties; the remainder of the thesis empirically investigates whether these behaviours are observable in human sentence processing. We first consider whether the results of existing studies might be attributable to SLCM behaviour. Such evaluation provides support for an HSPM architecture that includes this SLCM and allows us to determine which SLCM variant is empirically most plausible. Predictions are made, using this variant, to determine SLCM behaviour in the face of novel utterances; these predictions are then tested using a self-paced reading paradigm. The results of this experimentation fully support the inclusion of the SLCM in a model of the HSPM and are not compatible with other existing models. As the SLCM is a modular and statistical process, empirical evidence for the SLCM also directly supports an HSPM architecture which is modular and statistical. We therefore conclude that our results strongly support both the SLCM and the MSH. However, more work is needed, both to produce further evidence and to define the model further

Abductive Reasoning in Multiple Fault Diagnosis

Abductive reasoning involves generating an explanation for a given set of observations about the world. Abduction provides a good reasoning framework for many AI problems, including diagnosis, plan recognition and learning. This paper focuses on the use of abductive reasoning in diagnostic systems in which there may be more than one underlying cause for the observed symptoms. In exploring this topic, we will review and compare several different approaches, including Binary Choice Bayesian, Sequential Bayesian, Causal Model Based Abduction, Parsimonious Set Covering, and the use of First Order Logic. Throughout the paper we will use as an example a simple diagnostic problem involving automotive troubleshooting

Linguistic feature analysis for protein interaction extraction

<p>Abstract</p> <p>Background</p> <p>The rapid growth of the amount of publicly available reports on biomedical experimental results has recently caused a boost of text mining approaches for protein interaction extraction. Most approaches rely implicitly or explicitly on linguistic, i.e., lexical and syntactic, data extracted from text. However, only few attempts have been made to evaluate the contribution of the different feature types. In this work, we contribute to this evaluation by studying the relative importance of deep syntactic features, i.e., grammatical relations, shallow syntactic features (part-of-speech information) and lexical features. For this purpose, we use a recently proposed approach that uses support vector machines with structured kernels.</p> <p>Results</p> <p>Our results reveal that the contribution of the different feature types varies for the different data sets on which the experiments were conducted. The smaller the training corpus compared to the test data, the more important the role of grammatical relations becomes. Moreover, deep syntactic information based classifiers prove to be more robust on heterogeneous texts where no or only limited common vocabulary is shared.</p> <p>Conclusion</p> <p>Our findings suggest that grammatical relations play an important role in the interaction extraction task. Moreover, the net advantage of adding lexical and shallow syntactic features is small related to the number of added features. This implies that efficient classifiers can be built by using only a small fraction of the features that are typically being used in recent approaches.</p

Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies

An algorithm that learns from a set of examples should ideally be able to exploit the available resources of (a) abundant computing power and (b) domain-specific knowledge to improve its ability to generalize. Connectionist theory-refinement systems, which use background knowledge to select a neural network's topology and initial weights, have proven to be effective at exploiting domain-specific knowledge; however, most do not exploit available computing power. This weakness occurs because they lack the ability to refine the topology of the neural networks they produce, thereby limiting generalization, especially when given impoverished domain theories. We present the REGENT algorithm which uses (a) domain-specific knowledge to help create an initial population of knowledge-based neural networks and (b) genetic operators of crossover and mutation (specifically designed for knowledge-based networks) to continually search for better network topologies. Experiments on three real-world domains indicate that our new algorithm is able to significantly increase generalization compared to a standard connectionist theory-refinement system, as well as our previous algorithm for growing knowledge-based networks.Comment: See http://www.jair.org/ for any accompanying file