Abductive Learning Research Articles

Value management and common accounting performance measures for corporations

Abstract Management is aware that the value of firms is the ultimate measure of company performance. However, management has been using common accounting measures as an operating guide because the linkage between operational planning and value is vague and complex and, therefore, difficult to apply. Managers need to have clear targets and performance measures to track progress. This paper examines if a set of common accounting performance measures are linked tightly to the overall value of the firm. An abductive learning network (ALN) approach, an artificial intelligence technique, is used in this research because an ALN approach is non-parametric and can capture a subtle dynamic relationship between input and output variables, which is not obvious in linear statistical analysis. Empirical results show that an ALN model is very effective in synthesizing the value of the firm using six common accounting variables. The results also provide four strategic variables that can be used for devising strategic and operating plans to maximize the value of firms.

Integrating induction and abduction in logic programming

We propose an approach for the integration of abduction and induction in Logic Programming. We define an Abductive Learning Problem as an extended Inductive Logic Programming problem where both the background and target theories are abductive theories and where abductive derivability is used as the coverage relation instead of deductive derivability. The two main benefits of this integration are the possibility of learning in presence of incomplete knowledge and the increased expressive power of the background and target theories. We present the system LAP (Learning Abductive Programs) that is able to solve this extended learning problem and we describe, by means of examples, four different learning tasks that can be performed by the system: learning from incomplete knowledge, learning rules with exceptions, learning from integrity constraints and learning recursive predicates.

Abductive Machine Learning for Modeling and Predicting the Educational Score in School Health Surveys

The use of modern abductive machine learning techniques is described for modeling and predicting outcome parameters in terms of input parameters in medical survey data. The AIM (Abductory Induction Mechanism) abductive network machine-learning tool is used to model the educational score in a health survey of 2,720 Albanian primary school children. Data included the child's age, gender, vision, nourishment, parasite infection, family size, parents' education, and educational score. Models synthesized by training on just 100 cases predict the educational score output for the remaining 2,620 cases with 100% accuracy. Simple models represented as analytical functions highlight global relationships and trends in the survey population. Models generated are quite robust, with no change in the basic model structure for a 10-fold increase in the size of the training set. Compared to other statistical and neural network approaches, AIM provides faster and highly automated model synthesis, requiring little or no user intervention.

Assessing the Rental Value of Residential Properties: An Abductive Learning Networks Approach

This paper attempts to estimate rental value of residential properties using Abductive Learning Networks (ALN), an artificial intelligence technique. The results indicate that the ALN model provides an accurate estimation of rents with only seven input variables, while other multivariate statistical techniques do not. The ALN model automatically selects the best network structure, node types and coefficients, and therefore it simplifies the maintenance of the model. Once the final model is synthesized, the ALN model becomes very compact, rapidly executable and cost-effective.

Bayes' paradigm-based analysis of abductive learning

Il processo di abduzione e qui dettagliatamente analizzato, sia relativamente alla sua strurtura, sia alla funzione svolta dagli elementi che formano la struttura stessa. Detta analisi viene compiuta finalizzandola all'argomento nel quale questo articolo si colloca: ottenimento di un modello del processo in argomento, da utilizzare in Intelligenza Artificiale. L'approccio seguito considera il modello classico del processo stesso, cioe quello probabilistico dovuto a Bayes. In tal modo puo essere ottenuta una ideale trama sulla quale tessere inferenze abduttive. Lo scopo che in definitiva e perseguito e quello di pervenire ad un modello di apprendimento di natura abduttiva basato su dati probabilistici. Questo verra attuato alimentando la struttura ottenuta con funzione di appartenenza computabili, anziche con funzioni di probabilita.