Abductive reasoning

About: Abductive reasoning is a research topic. Over the lifetime, 1917 publications have been published within this topic receiving 44645 citations. The topic is also known as: abduction & abductive inference.

Abductive and approximate reasoning models for characterizing inference channels

Abstract: A serious problem in computer database and knowledge base security is detecting and eliminating so-called inference channels. The existence of such channels enables a user with access to information classified at a low level to infer information classified at a high level, and through the transformation of low level data to high level data may provide an unacceptable information flow. In order to estimate the presence of inference channels, determine the degree of risk which they present, and find ways to eliminate them, one needs a formal model to describe them. The authors introduce abductive reasoning. Abduction provides both the basis for a formal model for the inference problem and a computational mechanism for detecting inference channels. Abduction additionally provides a framework for reasoning with approximate and uncertain information, which enables them to extend the model for inference channels by taking into account the likelihood that a person might believe some statement of interest. >

Logic-based plan recognition for intelligent help systems

Abstract: Intelligent help systems aim at providing optimal help to the users of complex software application systems. In this context plan recognition is essential for a cooperative system behavior in that it allows to predict the user's future actions, to determine suboptimal action sequences or even serves as a basis for user-adapted tutoring or learning components. In this paper a new approach to incremental plan recognition based on a modal temporal logic is described. This logic allows for an abstract representation of plans including control structures such as loops and conditionals which makes it particularly well-suited for the above-mentioned tasks in command-language environments. There are two distinct phases: With a generalized abductive reasoning mechanism the set of valid plan hypotheses is determined in each recognition step. A probabilistic selection, based on Dempster-Shafer Theory, then serves to determine the "best" hypotheses in order to be able to provide help whenever required.

Reading comprehension tests for computer-based understanding evaluation

Abstract: Reading comprehension (RC) tests involve reading a short passage of text and answering a series of questions pertaining to that text. We present a methodology for evaluation of the application of modern natural language technologies to the task of responding to RC tests. Our work is based on ABCs (Abduction Based Comprehension system), an automated system for taking tests requiring short answer phrases as responses. A central goal of ABCs is to serve as a testbed for understanding the role that various linguistic components play in responding to reading comprehension questions. The heart of ABCs is an abductive inference engine that provides three key capabilities: (1) first-order logical representation of relations between entities and events in the text and rules to perform inference over such relations, (2) graceful degradation due to the inclusion of abduction in the reasoning engine, which avoids the brittleness that can be problematic in knowledge representation and reasoning systems and (3) system transparency such that the types of abductive inferences made over an entire corpus provide cues as to where the system is performing poorly and indications as to where existing knowledge is inaccurate or new knowledge is required. ABCs, with certain sub-components not yet automated, finds the correct answer phrase nearly 35 percent of the time using a strict evaluation metric and 45 percent of the time using a looser inexact metric on held out evaluation data. Performance varied for the different question types, ranging from over 50 percent on who questions to over 10 percent on what questions. We present analysis of the roles of individual components and analysis of the impact of various characteristics of the abductive proof procedure on overall system performance.

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.