Query: Multiple abductive reasoning

title: Abductive Commonsense Reasoning
abstract: Abductive reasoning is inference to the most plausible explanation. For example, if Jenny finds her house in a mess when she returns from work, and remembers that she left a window open, she can hypothesize that a thief broke into her house and caused the mess, as the most plausible explanation. While abduction has long been considered to be at the core of how people interpret and read between the lines in natural language (Hobbs et al. 1988) there has been relatively little research in support of abductive natural language inference and generation. We present the first study that investigates the viability of language based abductive reasoning. We introduce a challenge dataset, ART, that consists of over 20k commonsense narrative contexts and 200k explanations. Based on this dataset, we conceptualize two new tasks (i) Abductive NLI: a multiple choice question answering task for choosing the more likely explanation, and (ii) Abductive NLG: a conditional generation task for explaining given observations in natural language. On Abductive NLI, the best model achieves 68.9% accuracy, well below human performance of 91.4% On Abductive NLG, the current best language generators struggle even more, as they lack reasoning capabilities that are trivial for humans. Our analysis leads to new insights into the types of reasoning that deep pre trained language models fail to perform despite their strong performance on the related but more narrowly defined task of entailment NLI pointing to interesting avenues for future research.

title: Back to the Future: Unsupervised Backprop based Decoding for Counterfactual and Abductive Commonsense Reasoning
abstract: Abductive and counterfactual reasoning, core abilities of everyday human cognition, require reasoning about what might have happened at time t, while conditioning on multiple contexts from the relative past and future. However, simultaneous incorporation of past and future contexts using generative language models (LMs) can be challenging, as they are trained either to condition only on the past context or to perform narrowly scoped text infilling. In this paper, we propose DeLorean, a new unsupervised decoding algorithm that can flexibly incorporate both the past and future contexts using only off the shelf, left to right language models and no supervision. The key intuition of our algorithm is incorporating the future through back propagation, during which, we only update the internal representation of the output while fixing the model parameters. By alternating between forward and backward propagation, DeLorean can decode the output representation that reflects both the left and right contexts. We demonstrate that our approach is general and applicable to two nonmonotonic reasoning tasks: abductive text generation and counterfactual story revision, where DeLorean outperforms a range of unsupervised and some supervised methods, based on automatic and human evaluation.

title: ExplanationLP: Abductive Reasoning for Explainable Science Question Answering
abstract: We propose a novel approach for answering and explaining multiple choice science questions by reasoning on grounding and abstract inference chains. This paper frames question answering as an abductive reasoning problem, constructing plausible explanations for each choice and then selecting the candidate with the best explanation as the final answer. Our system, ExplanationLP, elicits explanations by constructing a weighted graph of relevant facts for each candidate answer and extracting the facts that satisfy certain structural and semantic constraints. To extract the explanations, we employ a linear programming formalism designed to select the optimal subgraph. The graphs' weighting function is composed of a set of parameters, which we fine tune to optimize answer selection performance. We carry out our experiments on the WorldTree and ARC Challenge corpus to empirically demonstrate the following conclusions: (1) Grounding Abstract inference chains provides the semantic control to perform explainable abductive reasoning (2) Efficiency and robustness in learning with a fewer number of parameters by outperforming contemporary explainable and transformer based approaches in a similar setting (3) Generalisability by outperforming SOTA explainable approaches on general science question sets.

title: Features of Modeling Based Abductive Reasoning as a Disciplinary Practice of Inquiry in Earth Science
abstract: The purpose of this study was to investigate the features of modeling based abductive reasoning as a disciplinary practice of inquiry in the domain of earth science. The study was based on an undergraduate course of a university of education, Korea, offered for preservice elementary teachers majoring in science as their specialty. The course enrollees participated in an inquiry project in which they were asked to abductively generate models representing past geologic events in order to explain how two units in a sedimentary rock outcrop had been formed. Three students were selected as major informants for the study, and multiple types of qualitative data were collected, including the students' sketchbook records, audio recording of whole class presentation and discussion, and interviews with the students. The data were analyzed according to the method of analytical induction, which yielded three assertions as the findings of the study. First, while an explanatory model can be generated by combining resource models, the combination of resource models does not necessarily result in a scientifically sound explanatory model. Second, a systemic approach can help activate a critical resource model which can in turn lead to a scientifically sound explanatory model. Third, simulations with a model can enhance the plausibility of the model. Based on these findings, causal combinations of resource models, a systemic approach to earth scientific problem solving, and simulations with a model are suggested and discussed as implications for science education and relevant research.

title: Understanding Clinical Reasoning from Multiple Perspectives: A Conceptual and Theoretical Overview
abstract: Rather than a historical overview as in Chap. 2, this chapter provides the reader with insight into the various approaches that have been used to understand clinical reasoning. We review concepts and major scholars who have been involved in such investigations. Cognitive psychologists Newel and Simon theorized about problem solving skills and artificial intelligence and initiated the use of computers as metaphors of thinking. Elstein and colleagues found that there is no such thing as a general problem solving skill, independent of medical knowledge, and thus clinical reasoning is case specific. Reasoning then became analyzed in approaches, including forward reasoning from data to diagnosis; hypothetico deductive reasoning with backward nature, from hypothesis to diagnosis; and abductive reasoning to understand early hypothesis generation that is so characteristic in clinical reasoning, elaborated by Patel and colleagues. Bordage introduced prototypes to characterize how physicians may remember illness presentations and semantic qualifiers to denote the shortened conceptual language and labels physicians use to store medical information systematically in memory. Illness scripts represent how encounters with diseases are remembered by physicians and were introduced by Feltovich and Barrows. Schmidt and Boshuizen elaborated the concept further and propose encapsulation of knowledge as a hypothetical process that happens when physicians regularly and routinely apply shortcuts in thinking typically ellaborated as pathophysiology. Reasoning ability appears not only to be case specific it is also situation or context specific. Clinicians with broad reasoning ability have extensive experience. Deliberate practice with many cases and in varying contexts is recommended by Ericsson to acquire reasoning expertise. To improve reasoning, some authors have focused on cognitive biases and error prevention. Norman, however, concludes that bias reduction strategies are unlikely to be successful but correcting knowledge deficiencies is likely to lead to reasoning success. Kahnemann promoted System 1 and System 2 thinking for instant pattern recognition (nonanalytic reasoning) and analytic reasoning, respectively. What actually happens in the brain during clinical reasoning is the domain of neuroscience, which may provide insights from research in the near future.

title: Abductive reasoning in multiple fault diagnosis
abstract: 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. 1 I n t r o d u c t i o n The technologies of knowledge based expert systems have been applied to many different types of problems. Diagnosis has been one of the earliest applications areas as well as being one of the most important and interesting. One at tempt to formalize diagnosis is as Abduction reasoning from a set of observations about the world to a hypothesis that explains or accounts for the observations. 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 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 use, as an example, a s imple diagnostic problem involving automotive troubleshooting adapted from ( 1984) Numerous expert systems have been developed for diagnostic reasoning, many of them in the medical area. Some of the earliest successful systems were rule based deductive programs, like MYCIN (Shortliffe, 1976, Buchanan Shortliffe, 1984) A common crit icism of these pioneering efforts was their handling of situations where more than one disease was needed to explain correctly all the observed symptoms. Each of the systems we discuss in this paper held as a major design objective the

title: Abductive reasoning in multiple fault diagnosis

title: Abductive reasoning in multiple fault diagnosis

title: An online intelligent alarm processing system based on abductive reasoning network
abstract: Large amounts of alarms coming into control center when faults occur have harassed operators for a long time, especially when there are false alarms, missing alarms or multiple faults. An online intelligent alarm processing system based on abductive reasoning network (ARN) is developed to deal with the alarms automatically. Models and real time data of the power system are collected from both supervisory control and data acquisition system energy management system (SCADA/EMS) and fault information system, including primary and secondary models. First, when alarms arrive, rules reflecting cause effect relationship between faults and alarms and between alarms themselves are established automatically, and the whole abductive reasoning network is constructed. Then, all the coming alarms are located on the network and traced back to the roots. Third, possible diagnoses are sifted and picked out according to their false alarm rate and missing alarm rate. At last, the diagnosis results are presented to the operators in several friendly manners. The system has been applied to the district control center of Jiaxing, China and is functioning well.

title: An online intelligent alarm processing system based on abductive reasoning network
abstract: Large amounts of alarms coming into control center when faults occur have harassed operators for a long time, especially when there are false alarms, missing alarms or multiple faults. An online intelligent alarm processing system based on abductive reasoning network (ARN) is developed to deal with the alarms automatically. Models and real time data of the power system are collected from both supervisory control and data acquisition system energy management system (SCADA/EMS) and fault information system, including primary and secondary models. First, when alarms arrive, rules reflecting cause effect relationship between faults and alarms and between alarms themselves are established automatically, and the whole abductive reasoning network is constructed. Then, all the coming alarms are located on the network and traced back to the roots. Third, possible diagnoses are sifted and picked out according to their false alarm rate and missing alarm rate. At last, the diagnosis results are presented to the operators in several friendly manners. The system has been applied to the district control center of Jiaxing, China and is functioning well.