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8 The paper introduces a knowledge engineering methodology to handle scenarios of inter-human interaction. We focus on the attitudes 9 of the conflicting agents, building the formal framework for the representation of these scenarios as sequences of mental actions of the 10 agents. Developed framework facilitates a domain-independent comparative analysis of(More)
We develop the means to mine for associative features in biological data. The hybrid reasoning schema for deterministic machine learning and its implementation via logic programming is presented. The methodology of mining for correlation between features is illustrated by the prediction tasks for protein secondary structure and phylogenetic profiles. The(More)
We employ the formalism of default logic to model certain phenomena of autistic reasoning. Our main finding is that while people with autism may be able to process single default rules, they have a characteristic difficulty in cases where multiple default rules conflict. Even though default reasoning was intended to simulate the reasoning of typical human(More)
We develop a graph representation and learning technique for parse structures for paragraphs of text. We introduce Parse Thicket (PT) as a sum of syntactic parse trees augmented by a number of arcs for inter-sentence word-word relations such as co-reference and taxonomic relations. These arcs are also derived from other sources, including Speech Act and(More)
We build an open-source toolkit which implements deterministic learning to support search and text classification tasks. We extend the mechanism of logical generalization towards syntactic parse trees and attempt to detect weak semantic signals from them. Generalization of syntactic parse tree as a syntactic similarity measure is defined as the set of(More)
In this paper, we apply concept learning techniques to solve a number of problems in the customer relationship management (CRM) domain. We present a concept learning technique to tackle common scenarios of interaction between conflicting human agents (such as customers and customer support representatives). Scenarios are represented by directed graphs with(More)
We apply reasoning about mental attributes to process the scenarios of multiagent conflicts. Our approach is illustrated by the domain of complaint analysis: rather advanced methods are required to determine whether complaint is valid or not. We demonstrate that information on mental actions and emotional states of conflicting agents is frequently(More)
A machine learning technique for handling scenarios of interaction between conflicting agents is suggested. Scenarios are represented by directed graphs with labeled vertices (for mental actions) and arcs (for temporal and causal relationships between these actions and their parameters). The relation between mental actions and their descriptions gives rise(More)