A comparative survey of integrated learning systems

This paper presents the duction framework for unifying the three basic forms of inference - deduction, abduction, and induction - by specifying the possible relationships and influences among them in the context of integrated learning. Special assumptive forms of inference are defined that extend the use of these inference methods, and the properties of these forms are explored. A comparison to a related inference-based learning frame work is made. Finally several existing integrated learning programs are examined in the perspective of the duction framework

Transforming Problem-Based Learning through Abductive Reasoning

Background: Hypothetico-deductive reasoning is the current approach for reasoning through care situations within problem-based learning (PBL). While this approach is widely used in both PBL and non-PBL curricula, abductive reasoning is recommended (as an alternative approach) due to its broader method for analyzing and explaining care situations within problem-based learning. Method: A step-by-step process rooted in abductive reasoning is proposed and demonstrated as a new way of examining and explaining care situations within problem-based learning. Results: The proposed strategy emphasizes the creation of hypotheses through phenomena detection, development of a causal model, identification of learning needs, recognition of salience, synthesis and reflection. Conclusion: Since the proposed approach has not been implemented previously, its practical implications require research attention which will contribute to the emerging field of abductive reasoning within nursing education. Résumé : Contexte : Dans l’apprentissage par problèmes (APP), le raisonnement hypothético-déductif est l’approche actuellement utilisée pour raisonner à partir de situations de soins. Or, bien que cette approche soit largement utilisée dans les programmes fondés sur l’APP et ceux qui ne le sont pas, le raisonnement abductif est recommandé (comme autre approche) puisque sa méthode d’analyse et d’explication des situations de soins au sein de l’APP est plus vaste. Méthode : Proposer et démontrer un processus étape par étape ancré dans le raisonnement abductif, comme une nouvelle manière d’analyser et d’expliquer des situations de soins dans le cadre de l’APP. Résultats : La stratégie proposée favorise la formulation d’hypothèses par la détection de phénomènes, la mise en place d’un modèle causal, l’identification des besoins d’apprentissage, la reconnaissance de la prépondérance, la synthèse et la réflexion.. Conclusion : Puisque l’approche proposée n’a pas été mise en place auparavant, ses implications pratiques nécessitent des recherches, qui contribueront au domaine émergent du raisonnement abductif dans le cadre de la formation en sciences infirmières

Integration of Abductive and Deductive Inference Diagnosis Model and Its Application in Intelligent Tutoring System

This dissertation presents a diagnosis model, Integration of Abductive and Deductive Inference diagnosis model (IADI), in the light of the cognitive processes of human diagnosticians. In contrast with other diagnosis models, that are based on enumerating, tracking and classifying approaches, the IADI diagnosis model relies on different inferences to solve the diagnosis problems. Studies on a human diagnosticians\u27 process show that a diagnosis process actually is a hypothesizing process followed by a verification process. The IADI diagnosis model integrates abduction and deduction to simulate these processes. The abductive inference captures the plausible features of this hypothesizing process while the deductive inference presents the nature of the verification process. The IADI diagnosis model combines the two inference mechanisms with a structure analysis to form the three steps of diagnosis, mistake detection by structure analysis, misconception hypothesizing by abductive inference, and misconception verification by deductive inference. An intelligent tutoring system, Recursive Programming Tutor (RPT), has been designed and developed to teach students the basic concepts of recursive programming. The RPT prototype illustrates the basic features of the IADI diagnosis approach, and also shows a hypertext-based tutoring environment and the tutoring strategies, such as concentrating diagnosis on the key steps of problem solving, organizing explanations by design plans and incorporating the process of tutoring into diagnosis

A layered abduction model of perception: Integrating bottom-up and top-down processing in a multi-sense agent

A layered-abduction model of perception is presented which unifies bottom-up and top-down processing in a single logical and information-processing framework. The process of interpreting the input from each sense is broken down into discrete layers of interpretation, where at each layer a best explanation hypothesis is formed of the data presented by the layer or layers below, with the help of information available laterally and from above. The formation of this hypothesis is treated as a problem of abductive inference, similar to diagnosis and theory formation. Thus this model brings a knowledge-based problem-solving approach to the analysis of perception, treating perception as a kind of compiled cognition. The bottom-up passing of information from layer to layer defines channels of information flow, which separate and converge in a specific way for any specific sense modality. Multi-modal perception occurs where channels converge from more than one sense. This model has not yet been implemented, though it is based on systems which have been successful in medical and mechanical diagnosis and medical test interpretation

Abductive Reasoning with the GPT-4 Language Model: Case studies from criminal investigation, medical practice, scientific research

This study evaluates the GPT-4 Large Language Model's abductive reasoning in complex fields like medical diagnostics, criminology, and cosmology. Using an interactive interview format, the AI assistant demonstrated reliability in generating and selecting hypotheses. It inferred plausible medical diagnoses based on patient data and provided potential causes and explanations in criminology and cosmology. The results highlight the potential of LLMs in complex problem-solving and the need for further research to maximize their practical applications.Comment: The article is 12 pages long and has one figure. It also includes a link to some ChatGPT dialogues that show the experiments that support the article's findings. The article will be published in V. Bambini and C. Barattieri di San Pietro (eds.), Sistemi Intelligenti, Special Section "Multidisciplinary perspectives on ChatGPT and the family of Large Language Models

ARROWSMITH-P: A prototype expert system for software engineering management

Although the field of software engineering is relatively new, it can benefit from the use of expert systems. Two prototype expert systems were developed to aid in software engineering management. Given the values for certain metrics, these systems will provide interpretations which explain any abnormal patterns of these values during the development of a software project. The two systems, which solve the same problem, were built using different methods, rule-based deduction and frame-based abduction. A comparison was done to see which method was better suited to the needs of this field. It was found that both systems performed moderately well, but the rule-based deduction system using simple rules provided more complete solutions than did the frame-based abduction system

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