Deductive Generalization Research Articles

SYSTEM APPROACH TO DESIGNING TECHNICAL MEANS OF SEEDING

In the conditions of market relations, agricultural enterprises begin to independently focus on the recommendations of agricultural science using their own experience and choose the necessary machines and implements for crop production technologies that meet the requirements for productivity, reliability and economic capabilities of farms. In agricultural science and practice, a large number of experimental factors have accumulated, which are taken into account when designing new technological processes and working bodies. Currently, in connection with the automation of design, there is an urgent need to comprehend the accumulated experience and build the foundations of the theory of designing technologies and machines, revealing the basic laws of the design process. So, based on the essence of the systemic approach, a methodology has been developed, where, due to deductive generalization of the results obtained by agricultural science and practice, that is, «top — down» using the formed principles of the whole system and more specific statements that reveal the structure and content of design solutions. The structure of the structure of the principle of compatibility is described, which is characterized by internal integrity, due to the interconnectedness of the principle and statements of various levels.

How Inductive and Deductive Generalization Shape the Guilt-by-Association Phenomenon Among Firms: Theory and Evidence

This study advances and tests the notion that the phenomenon of guilt by association-- whereby innocent organizations are penalized due to their similarity to offending organizations-- is shaped by two distinct forms of generalization. We analyze how and why evaluators’ interpretative process following instances of corporate misconduct will likely include not only inductive generalization (rooted in similarity judgments and prototype-based categorization) but also deductive generalizing (rooted in evaluators’ theories and causal-based categorization). We highlight the role and relevance of this neglected distinction by extending guilt-by-association predictions to include two unique predictions based on deductive generalization. First, we posit a recipient effect: if an innocent organization falls under a negative stereotype that causally links the innocent firm with corporate misconduct, then that innocent firm will suffer a greater negative spillover effect, irrespective of its similarity to the offending firm. Second, we also posit a transmission effect: if the offending firm falls under the same negative stereotype, then the negative spillover effect to other similar firms will be lessened. We also analyze how media discourse can foster negative stereotypes, and thus amplify these two effects. We find support for our hypotheses in an analysis of stock market reactions to corporate misconduct for all U.S. and international firms using reverse mergers to gain publicly traded status in the United States. We discuss the implications of our theoretical perspective and empirical findings for research on corporate misconduct, guilt by association, and stock market prejudice.

Experience-Based Planning Domains: an Integrated Learning and Deliberation Approach for Intelligent Robots

Deliberation and learning are required to endow a robot with the capabilities for acquiring knowledge, performing a variety of tasks and interactions, and adapting to open-ended environments. This paper presents the notion of experience-based planning domains (EBPDs) for task level learning and planning in robotics. EBPDs provide methods for a robot to: (i) obtain robot activity experiences from the robot's performance in a dynamic environment; (ii) conceptualize each experience producing an activity schema; and (iii) exploit the learned activity schemata to make plans in similar situations. Experiences are episodic descriptions of plan-based robot activities including environment perception, sequences of applied actions and achieved tasks. The conceptualization approach integrates different techniques including deductive generalization, abstraction, goal inference and feature extraction. A high-level task planner was developed to find a solution for a task by following an activity schema. The proposed approach is illustrated and evaluated in a restaurant environment where a service robot learns how to carry out complex tasks.

Experience-Based Robot Task Learning and Planning with Goal Inference

Learning and deliberation are required to endow a robotwith the capabilities to acquire knowledge, perform a variety of tasks and interactions, and adapt to open-ended environments. This paper explores the notion of experience-based planning domains (EBPDs) for task-level learning and planning in robotics. EBPDs rely on methods for a robot to: (i) obtain robot activity experiences from the robot's performance; (ii) conceptualize each experience to a task model called activity schema; and (iii) exploit the learned activity schemata to make plans in similar situations. Experiences are episodic descriptions of plan-based robot activities including environment perception, sequences of applied actions and achieved tasks. The conceptualization approach integrates different techniques including deductive generalization, abstraction and feature extraction to learn activity schemata. A high-level task planner was developed to find a solution for a similar task by following an activity schema. In this paper, we extend our previous approach by integrating goal inference capabilities. The proposed approach is illustrated in a restaurant environment where a service robot learns how to carry out complex tasks.

A scalable method for deductive generalization in the spreadsheet paradigm

In this paper, we present an efficient method for automatically generalizing programs written in spreadsheet languages. The strategy is to do generalization through incremental analysis of logical relationships among concrete program entities from the perspective of a particular computational goal. The method uses deductive dataflow analysis with algebraic back-substitution rather than inference with heuristics, and there is no need for generalization-related dialog with the user. We present the algorithms and their time complexities and show that, because the algorithms perform their analyses incrementally, on only the on-screen program elements rather than on the entire program, the method is scalable. Performance data is presented to help demonstrate the scalability.

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Appendices A--D

A scalable method for deductive generalization in the spreadsheet paradigm

The following abstracts are from recent issues and the forthcoming issue of ACM's Transactions o f Computer Human Interaction (ToCHI). They are included here to alert interactions' readers to what research is being done in the field of Computer Human Interaction. The complete papers, when published, can be found in ACM's Digital Library at www.acm.org/pubs/contents/journals/tochi/

Inferential theory of learning as a conceptual basis for multistrategy learning

In view of a great proliferation of machine learning methods and paradigms, there is a need for a general conceptual framework that would explain their interrelationships and provide a basis for their integration into multistrategy learning systems. This article presents initial results on theInferential Theory of Learning that aims at developing such a framework, with the primary emphasis on explaining logical capabilities of learning systems, i.e., theircompetence. The theory views learning as a goal-oriented process of modifying the learner's knowledge by exploring the learner's experience. Such a process is described as a search through aknowledge space, conducted by applying knowledge transformation operators, calledknowledge transmutations. Transmutations can be performed using any type of inference—deduction, induction, or analogy. Several fundamental pairs of transmutations are presented in a novel and very general way. These include generalization and specialization, explanation and prediction, abstraction and concretion, and similization and dissimilization. Generalization and specialization transmutations change thereference set of a description (the set of entities being described). Explanations and predictions derive additional knowledge about the reference set (explanatory or predictive). Abstractions and concretions change the level of detail in describing a reference set. Similizations and dissimilizations hypothesize knowledge about a reference set based on its similarity or dissimilarity with another reference set. The theory provides a basis formultistrategy task-adaptive learning (MTL), which is outlined and illustrated by an example. MTL dynamically adapts strategies to thelearning task, defined by the input information, the learner's background knowledge, and the learning goal. It aims at synergistically integrating a wide range of inferential learning strategies, such as empirical and constructive inductive generalization, deductive generalization, abductive derivation, abstraction, similization, and others.

Généralisation inductive et déductive dans un apprentissage par l'action

Summary : Inductive and deductive generalization in learning by action. The objective of this research was, firstly, to study the effect of feedback when the tolerance of errors allows accidental successes, and secondly to appraise the contribution of various mechanisms of learning. Thirty subjects play a game of strategy against E for 25 rounds. The deviations from the winning strategy form a hierarchy of six kinds of errors, A B C D E. In the conditions I, II, III, E penalizes respectively the errors AB, ABCD or ABCDE, by systematically choosing the winning strategy ; so the probability that S can win a round when he does not yet know the winning strategy, decreases from condition I to condition III. The terminal level of learning is poor in condition I, medium in the two other conditions. The analysis of the results attempts to identify the cognitive activities at work, especially the categories of inductive or deductive generalization, and their relation with the terminal level of learning. Key words : learning, generalisation.