Showing papers on "Abductive reasoning published in 2011"

Compositional Shape Analysis by Means of Bi-Abduction

Abstract: The accurate and efficient treatment of mutable data structures is one of the outstanding problem areas in automatic program verification and analysis. Shape analysis is a form of program analysis that attempts to infer descriptions of the data structures in a program, and to prove that these structures are not misused or corrupted. It is one of the more challenging and expensive forms of program analysis, due to the complexity of aliasing and the need to look arbitrarily deeply into the program heap. This article describes a method of boosting shape analyses by defining a compositional method, where each procedure is analyzed independently of its callers. The analysis algorithm uses a restricted fragment of separation logic, and assigns a collection of Hoare triples to each procedure; the triples provide an over-approximation of data structure usage. Our method brings the usual benefits of compositionality---increased potential to scale, ability to deal with incomplete programs, graceful way to deal with imprecision---to shape analysis, for the first time.The analysis rests on a generalized form of abduction (inference of explanatory hypotheses), which we call bi-abduction. Bi-abduction displays abduction as a kind of inverse to the frame problem: it jointly infers anti-frames (missing portions of state) and frames (portions of state not touched by an operation), and is the basis of a new analysis algorithm. We have implemented our analysis and we report case studies on smaller programs to evaluate the quality of discovered specifications, and larger code bases (e.g., sendmail, an imap server, a Linux distribution) to illustrate the level of automation and scalability that we obtain from our compositional method.This article makes number of specific technical contributions on proof procedures and analysis algorithms, but in a sense its more important contribution is holistic: the explanation and demonstration of how a massive increase in automation is possible using abductive inference.

Theorizing in design science research

Abstract: Theory is a central element in research. Due to the importance of theory in research, considerable efforts have been made to better understand the process of theorizing, i.e., development of a theory. A review of the literature in this area suggests that two dominant theorizing approaches are anchored to deductive and inductive reasoning respectively. In contrast, an essential part of theorizing for design may involve abductive reasoning. The purpose of design theory is not to advance declarative logic regarding truth or falseness, but to guide learning and problem solving through the conceptualization of a design artifact. This paper critically examines the process of theorizing for design by developing an idealized design theorizing framework. The framework indicates that theorizing for design operates in two distinct domains: instance and abstract. Further, four key theorizing activities are identified in this framework: abstraction, solution search, de-abstraction, and registration. The framework provides grounds for building strong design theories in the design science paradigm by explicating the underlying theorizing process for design.

Exposing the Magic of Design: A Practitioner's Guide to the Methods and Theory of Synthesis

Abstract: Introduction How to make sense of chaos A lack of method in practice leads to problems The goals of this text The immediacy of this text Section One: What is synthesis? Chapter 1: A Theory of Synthesis Understanding how people solve problems Acting on an informed hunch Making a judgment Using partial or incomplete information Understanding, and breaking, constraints Chapter 2: Sensemaking, Frames, Models and Patterns The nature of sensemaking in understanding The role of perspective in framing situations The importance of models in sensemaking Mental models as a specific type of cognitive representation The nature of patterns on our experiences Chapter 3: Abductive Reasoning Section Two: Design synthesis in a business context Chapter 4: The value of synthesis in driving innovation What is innovation? Design synthesis links innovation research and design Chapter 5: The culture of synthesis Challenging constraints and questioning purpose Being playful Experiencing flow Using visualization as a primary mechanism of thought Changing a prohibitive culture Section Three: Methods and applicability A framework for organizing synthesis methods A method selection guide Chapter 6: Methods for making meaning out of data Externalizing the process (get out of your laptop!) Using visual design to clean up the mess Organizing, to produce semantic relationships Prioritizing the data to emphasize what's important Judging the data, to reduce the quantity Enhancing the data through "best guess" intuitive leaps For Example: Getting to Meaning through Story Method: Affinity Diagramming How to apply this method For Example: Parallel Clustering Method: Flow Diagramming How to Apply this Method For Example: The Flow Through a Hunter Fan Thermostat A Case Study Chapter 7: Methods for building an experience framework Telling a Story. Changing the Scale. Shifting the Placements. Method: Concept Mapping How to Apply this Method For Example - Using Concept Maps in Product Development Method: Forced Semantic Zoom ("Ecosystem Mapping") How to Apply this Method For Example: Breakpoint Diagrams and Other Tools for Transitions Method: Forced Temporal Zoom ("Customer Journey Mapping") How to Apply this Method For Example -The Emotional Touch Points of Shopping Chapter 8: Methods for Creating Empathy and Insight Understanding Chasm 3: Empathy and Insight Method: Reframing How to Apply this Method Method: Insight Combination How to Apply this Method Conclusion Glossary Works Cited

How Can Significance Tests Be Deinstitutionalized

Abstract: The purpose of this paper is to propose possible solutions to the methodological problem of null hypothesis significance testing (NHST), which is framed as deeply embedded in the institutional structure of the social and organizational sciences. The core argument is that, for the deinstitutionalization of statistical significance tests, minor methodological changes within an unreformed epistemology will be as unhelpful as emotive exaggerations of the ill effects of NHST. Instead, several institutional-epistemological reforms affecting cultural-cognitive, normative, and regulative processes and structures in the social sciences are necessary and proposed in this paper. In the conclusion, the suggested research reforms, ranging from greater emphasis on inductive/abductive reasoning to statistical modeling and Bayesian epistemology, are classified according to their practical importance and the time horizon expected for their implementation. Individual-level change in researchers' use of NHST is unlikely if it is not facilitated by these broader epistemological changes.

Ontology-based context representation and reasoning for object tracking and scene interpretation in video

Abstract: Research highlights? We have developed a general framework for Computer Vision systems. ? Perceived and contextual knowledge is represented with ontologies. ? Rule-based reasoning is applied to achieve scene interpretation and vision enhancement. ? The framework can be extended and applied in different application domains. Computer vision research has been traditionally focused on the development of quantitative techniques to calculate the properties and relations of the entities appearing in a video sequence. Most object tracking methods are based on statistical methods, which often result inadequate to process complex scenarios. Recently, new techniques based on the exploitation of contextual information have been proposed to overcome the problems that these classical approaches do not solve. The present paper is a contribution in this direction: we propose a Computer Vision framework aimed at the construction of a symbolic model of the scene by integrating tracking data and contextual information. The scene model, represented with formal ontologies, supports the execution of reasoning procedures in order to: (i) obtain a high-level interpretation of the scenario; (ii) provide feedback to the low-level tracking procedure to improve its accuracy and performance. The paper describes the layered architecture of the framework and the structure of the knowledge model, which have been designed in compliance with the JDL model for Information Fusion. We also explain how deductive and abductive reasoning is performed within the model to accomplish scene interpretation and tracking improvement. To show the advantages of our approach, we develop an example of the use of the framework in a video-surveillance application.

Abductive markov logic for plan recognition

Abstract: Plan recognition is a form of abductive reasoning that involves inferring plans that best explain sets of observed actions. Most existing approaches to plan recognition and other abductive tasks employ either purely logical methods that do not handle uncertainty, or purely probabilistic methods that do not handle structured representations. To overcome these limitations, this paper introduces an approach to abductive reasoning using a first-order probabilistic logic, specifically Markov Logic Networks (MLNs). It introduces several novel techniques for making MLNs efficient and effective for abduction. Experiments on three plan recognition datasets show the benefit of our approach over existing methods.

Abductive reasoning with a large knowledge base for discourse processing

Abstract: This paper presents a discourse processing framework based on weighted abduction. We elaborate on ideas described in Hobbs et al. (1993) and implement the abductive inference procedure in a system called Mini-TACITUS. Particular attention is paid to constructing a large and reliable knowledge base for supporting inferences. For this purpose we exploit such lexical-semantic resources as WordNet and FrameNet. We test the proposed procedure and the obtained knowledge base on the Recognizing Textual Entailment task using the data sets from the RTE-2 challenge for evaluation. In addition, we provide an evaluation of the semantic role labeling produced by the system taking the Frame-Annotated Corpus for Textual Entailment as a gold standard.

Four Problems of Abduction: A Brief History

Abstract: Debates concerning the character, scope, and warrant of abductive inference have been active since Peirce first proposed that there was a third form of inference, distinct from induction and deduction. Abductive reasoning has been dubbed weak, incoherent, and even nonexistent. Part, at least, of the problem of articulating a clear sense of abductive inference is due to difficulty in interpreting Peirce. Part of the fault must lie with his critics, however. While this article will argue that Peirce indeed left a number of puzzles for interpreters, it will also contend that interpreters should be careful to distinguish discussion of the formal and strictly epistemic question of whether and how abduction is a sound form of inference from discussions of the practical goals of abduction, as Peirce understood them. This article will trace a history of critics and defenders of Peirce’s notion of abduction and discuss how Peirce both fueled the confusion and in fact anticipated and responded to several recurring ...

Characteristics of abductive inquiry in earth science: An undergraduate case study

Abstract: The goal of this case study was to describe characteristic features of abductive inquiry learning activities in the domain of earth science. Participants were undergraduate junior and senior students who were enrolled in an earth science education course offered for preservice secondary science teachers at a university in Korea. The undergraduate students conducted, as a course activity, earth scientific inquiry according to the Abductive Inquiry Model (AIM) to explain a typhoon's anomalous path. Data sources included students' presentation materials, written reports, and interviews with five selected participants. The data were analyzed qualitatively in collaboration with a practicing earth scientist. The findings of the study revealed the characteristics of students' inquiry performance in each phase of the AIM. During the exploration phase, the students investigated earth scientific phenomena with provided data and transformed the data into new forms to discover problems to be explained abductively. In the examination phase, the students activated and expanded their background knowledge to find appropriate rules for abductive inference. Furthermore, they created new rules, which contained hypothetical explanations for the phenomena in question. The selection phase provided the students with opportunities to evaluate their hypotheses with empirical and theoretical criteria and choose more plausible ones. Finally, in the explanation phase, the students provided genetic and narrative explanations using the hypotheses selected previously. Implications for science inquiry learning as well as relevant research were suggested. © 2010 Wiley Periodicals, Inc. Sci Ed95: 409–430, 2011

Towards practical ABox abduction in large OWL DL ontologies

Abstract: ABox abduction is an important aspect for abductive reasoning in Description Logics (DLs). It finds all minimal sets of ABox axioms that should be added to a background ontology to enforce entailment of a specified set of ABox axioms. As far as we know, by now there is only one ABox abduction method in expressive DLs computing abductive solutions with certain minimality. However, the method targets an ABox abduction problem that may have infinitely many abductive solutions and may not output an abductive solution in finite time. Hence, in this paper we propose a new ABox abduction problem which has only finitely many abductive solutions and also propose a novel method to solve it. The method reduces the original problem to an abduction problem in logic programming and solves it with Prolog engines. Experimental results show that the method is able to compute abductive solutions in benchmark OWL DL ontologies with large ABoxes.

On the representation and exploitation of context knowledge in a harbor surveillance scenario

Abstract: Maritime surveillance involves gathering and integrating a large amount of heterogeneous information of variable quality to provide diverse decision makers with reliable knowledge about situations and threats. This requires information processing at all fusion levels while taking into account contextual information. Context is especially important for harbor surveillance, one of the most challenging maritime scenarios due to the high number of different vessel types, the coexistence of very diverse operations, the multiple agencies and countries involved, etc. Successful processing of both contextual and transient observed information requires a reusable representation of the harbor domain, as well as effective reasoning methods. This paper discusses an approach to designing a hybrid harbor surveillance system combining ontology-based context representation, deductive reasoning for detection of abnormal objects from their characteristics and behavior, and abductive reasoning under uncertainty.

Theorising and practitioners in HRD: the role of abductive reasoning

Abstract: Purpose – The purpose of this paper is to argue that abductive reasoning is a typical but usually unrecognised process used by HRD scholars and practitioners alike.Design/methodology/approach – This is a conceptual paper that explores recent criticism of traditional views of theory‐building, based on the privileging of scientific theorising, which has led to a relevance gap between scholars and practitioners. The work of Charles Sanders Peirce and the varieties of an abductive reasoning process are considered.Findings – Abductive reasoning, which precedes induction and deduction, provide a potential connection with HRD practitioners who face difficult problems. Two types of abductive reasoning are explored – existential and analogic. Both offer possibilities for theorising with HRD practitioners. A range of methods for allowing abduction to become more evident with practitioners are presented. The authors consider how abduction can be used in engaged and participative research strategies.Research limitati...

Interleaved inductive-abductive reasoning for learning complex event models

Abstract: We propose an interleaved inductive-abductive model for reasoning about complex spatio-temporal narratives. Typed Inductive Logic Programming (Typed-ILP) is used as a basis for learning the domain theory by generalising from observation data, whereas abductive reasoning is used for noisy data correction by scenario and narrative completion thereby improving the inductive learning to get semantically meaningful event models. We apply the model to an airport domain consisting of video data for 15 turn-arounds from six cameras simultaneously monitoring logistical processes concerned with aircraft arrival, docking, departure etc and a verbs data set with 20 verbs enacted out in around 2500 vignettes. Our evaluation and demonstration focusses on the synergy afforded by the inductive-abductive cycle, whereas our proposed model provides a blue-print for interfacing common-sense reasoning about space, events and dynamic spatio-temporal phenomena with quantitative techniques in activity recognition.

An Abductive Model for Human Reasoning

Abstract: In this paper we contribute to bridging the gap between human reasoning as studied in Cognitive Science and commonsense reasoning based on formal logics and formal theories. In particular, the suppression task studied in Cognitive Science provides an interesting challenge problem for human reasoning based on logic. The work presented in the paper is founded on the recent approach by Stenning and van Lambalgen to model human reasoning by means of logic programs with a specific three-valued completion semantics and a semantic fixpoint operator that yields a least model, as well as abduction. Their approach has been subsequently made more precise and technically accurate by switching to three-valued Łukasiewicz logic. In this paper, we extend this refined approach by abduction. We show that the inclusion of abduction permits to adequately model additional empiric results reported from Cognitive Science. For the arising abductive reasoning tasks we give complexity results. Finally, we outline several open research issues that emerge from the application of logic to model human reasoning.

ILP-based reasoning for weighted abduction

Abstract: Abduction is widely used in the task of plan recognition, since it can be viewed as the task of finding the best explanation for a set of observations. The major drawback of abduction is its computational complexity. The task of abductive reasoning quickly becomes intractable as the background knowledge is increased. Recent efforts in the field of computational linguistics have enriched computational resources for commonsense reasoning. The enriched knowledge base facilitates exploring practical plan recognition models in an open-domain. Therefore, it is essential to develop an efficient framework for such large-scale processing. In this paper, we propose an efficient implementation ofWeighted abduction. Our framework transforms the problem of explanation finding in Weighted abduction into a linear programming problem. Our experiments showed that our approach efficiently solved problems of plan recognition and outperforms state-of-the-art tool for Weighted abduction.

Abductive plan recognition by extending Bayesian logic programs

Abstract: Plan recognition is the task of predicting an agent's top-level plans based on its observed actions. It is an abductive reasoning task that involves inferring cause from effect. Most existing approaches to plan recognition use either first-order logic or probabilistic graphical models. While the former cannot handle uncertainty, the latter cannot handle structured representations. In order to overcome these limitations, we develop an approach to plan recognition using Bayesian Logic Programs (BLPs), which combine first-order logic and Bayesian networks. Since BLPs employ logical deduction to construct the networks, they cannot be used effectively for plan recognition. Therefore, we extend BLPs to use logical abduction to construct Bayesian networks and call the resulting model Bayesian Abductive Logic Programs (BALPs). We learn the parameters in BALPs using the Expectation Maximization algorithm adapted for BLPs. Finally, we present an experimental evaluation of BALPs on three benchmark data sets and compare its performance with the state-of-the-art for plan recognition.

SCARE: A Case Study with Baghdad

Abstract: In this paper we introduce SCARE — the Spatial Cultural Abductive Reasoning Engine, which solves spatial abduction problems (Shakarian, Subrahmanian, and Sapino 2009). We review results of SCARE for activities by Iranian-sponsored “Special Groups” (Kagan, Kagan, and Pletka 2008) operating throughout the Baghdad urban area and compare these findings with new experiments where we predict IED cache sites of the Special Groups in Sadr City. We find that by localizing the spatial abduction problem to a smaller area we obtain greater accuracy - predicting cache sites within 0.33 km as opposed to 0.72 km for all of Baghdad. We suspect that local factors of physical and cultural geography impact reasoning with spatial abduction for this problem.

Empowering Adaptive Manufacturing with Interactive Diagnostics: A Multi-Agent Approach

Abstract: This paper presents a novel approach towards proactive manufacturing control that integrates automated diagnostics with human interaction, resulting in a flexible adaptation of machine capabilities which helps to avoid damage in case of abnormalities. The model-based interpretation process supports predictive diagnostics using abductive reasoning, relying on plausibility thresholds and human intervention to resolve the resulting ambiguity between competing solutions. This enables the system to detect and avoid potential failure states before they actually occur. The proposed architecture additionally integrates intelligent products as mobile sensors, improving robustness and dependability of the production system.

Abductive Reasoning for Continual Dialogue Understanding.

Abstract: In this paper we present a continual context-sensitive abductive framework for understanding situated spoken natural dialogue The framework builds up and refines a set of partial defeasible explanations of the spoken input, trying to infer the speaker's intention These partial explanations are conditioned on the eventual verification of the knowledge gaps they contain This verification is done by executing test actions, thereby going beyond the initial context The approach is illustrated by an example set in the context of human-robot interaction

The complexity of abduction for separated heap abstractions

Abstract: Abduction, the problem of discovering hypotheses that support a conclusion, has mainly been studied in the context of philosophical logic and Artificial Intelligence. Recently, it was used in a compositional program analysis based on separation logic that discovers (partial) pre/post specifications for un-annotated code which approximates memory requirements. Although promising practical results have been obtained, completeness issues and the computational hardness of the problem have not been studied. We consider a fragment of separation logic that is representative of applications in program analysis, and we study the complexity of searching for feasible solutions to abduction. We show that standard entailment is decidable in polynomial time, while abduction ranges from NP-complete to polynomial time for different sub-problems.

The Scope and Limits of the No Miracles Argument1

Abstract: In this paper, I review the scope and limits of the no miracles argument. I defend and, where necessary, revise my account of it as a way to justify Inference to the Best Explanation (IBE).

Representation of Parsimonious Covering Theory in OWL-DL

Abstract: The Web Ontology Language has not been designed for representing abductive inference, which is often required for applications such as medical disease diagnosis. As a consequence, existing OWL ontologies have limited ability to encode knowledge for such applications. In the last 150 years, many logic frameworks for the representation of abductive inference have been developed. Among these frameworks, Parsimonious Covering Theory (PCT) has achieved wide recognition. PCT is a formal model of diagnostic reasoning in which knowledge is represented as a network of causal associations, and whose goal is to account for observed symptoms with plausible explanatory hypotheses. In this paper, we argue that OWL does provide some of the expressivity required to approximate diagnostic reasoning, and outline a suitable encoding of PCT in OWL-DL.

Bayesian abductive logic programs: a probabilistic logic for abductive reasoning

Abstract: In this proposal, we introduce Bayesian Abductive Logic Programs (BALP), a probabilistic logic that adapts Bayesian Logic Programs (BLPs) for abductive reasoning. Like BLPs, BALPs also combine first-order logic and Bayes nets. However, unlike BLPs, which use deduction to construct Bayes nets, BALPs employ logical abduction. As a result, BALPs are more suited for problems like plan/activity recognition that require abductive reasoning. In order to demonstrate the efficacy of BALPs, we apply it to two abductive reasoning tasks - plan recognition and natural language understanding.

Relaxed Abduction: Robust Information Interpretation for Incomplete Models.

Abstract: This paper introduces relaxed abduction, a novel non-standard reasoning task for description logics. Although abductive reasoning over description logic knowledge bases has been applied successfully to various information interpretation tasks, it typically fails to provide adequate (or even any) results when confronted with spurious information or incomplete models. Relaxed abduction addresses this flaw by ignoring such pieces of information automatically based on a joint optimization of the sets of explained observations and required assumptions. We present a method to solve relaxed abduction over EL TBoxes based on the notion of multi-criterion shortest hyperpaths.