Reverse inference is a crucial inferential strategy used in cognitive neuroscience to derive conclusions about the engagement of processes from patterns brain activation. While widely employed experimental studies, it now viewed with increasing scepticism within community. One problem reverse that logically invalid, being an instance abduction Peirce’s sense. In this paper, we offer first syste...

We propose a multivariate decision tree inference scheme by using the minimum message length (MML) principle (Wallace and Boulton, 1968; Wallace and Dowe, 1999). The scheme uses MML coding as an objective (goodness-of-fit) function on model selection and searches with a simple evolution strategy. We test our multivariate tree inference scheme on UCI machine learning repository data sets and com...

Automated negotiation by autonomous agents has become increasingly important since the advent of e-marketplace. In this study, automated negotiation is viewed as a search process in which negotiators jointly search for a mutually acceptable contract in a multidimensional space formed by negotiable issues. This search is formulated as a multiple-objective decision making problem and is solved th...

A credal network associates convex sets of probability distributions with graph-based models. Inference with credal networks aims at determining intervals on probability measures. Here we describe how a branch-and-bound based approach can be applied to accomplish approximated inference in polytrees iteratively. Our strategy explores a breadth-first version of branch-and-bound to compute outer a...

Global ocean salinity/temperature variations are attracting increasing attention, due to their influence on ocean-atmospheric changes and their potential for improved climate forecasting. The goal is to analyze historic salinity/temperature data to make predictions about future variations. Traditional statistical models that assume data independence are not applicable as ocean data are often in...

Reasoning plays a central role in intelligent systems that operate in complex situations that involve time constraints. In this paper, we present the Adaptive Logic Interpreter, a reasoning system that acquires a controlled inference strategy adapted to the scenario at hand, using a variation on relational reinforcement learning. Employing this inference mechanism in a reactive agent architectu...

The modern statistical literature is replete with methods for performing variable selection and prediction in standard regression problems. However, simple models may misspecify or fail to capture important aspects of the data generating process such as missingness, correlation, and over/underdispersion. This realization has motivated the development of a large class of estimating equations whi...

Powerful new technologies, such as DNA microarrays, provide simple and economical ways to explore gene expression patterns on a genomic scale[1, 2]. Using comprehensive gene expression data, various approaches are planned to infer genetic networks [3, 4]. In this poster, we propose a system named AIGNET (Algorithms for Inference of Genetic Networks), and introduce two top down approaches for in...

For first-order Horn clauses without equality, resolution is complete with an arbitrary selection of a single literal in each clause [dN 96]. Here we extend this result to the case of clauses with equality for superposition-based inference systems. Our result is a generalization of the result given in [BG 01]. We answer their question about the completeness of a superposition-based system for g...

There is a need for modeling and reasoning on the subjective, incomplete, imprecise and vague nature of item features, user behavior and their relationships in machine learning algorithms. This paper presents results of analysis of effects of fuzzy theoretic inference/recommendation strategies and fuzzy theoretic similarity measures on a recommender system's accuracy-measured in terms of precis...