Logical analysis of data as a tool for the analysis of probabilistic discrete choice behavior

Probabilistic Discrete Choice Models (PDCM) have been extensively used to interpret the behavior of heterogeneous decision makers that face discrete alternatives. The classification approach of Logical Analysis of Data (LAD) uses discrete optimization to generate patterns, which are logic formulas characterizing the different classes. Patterns can be seen as rules explaining the phenomenon under analysis. In this work we discuss how LAD can be used as the first phase of the specification of PDCM. Since in this task the number of patterns generated may be extremely large, and many of them may be nearly equivalent, additional processing is necessary to obtain practically meaningful information. Hence, we propose computationally viable techniques to obtain small sets of patterns that constitute meaningful representations of the phenomenon and allow to discover significant associations between subsets of explanatory variables and the output. We consider the complex socio-economic problem of the analysis of the utilization of the Internet in Italy, using real data gathered by the Italian National Institute of Statistics

The Effect of Story Narrative in Multimedia Learning

abstract: ELearning, distance learning, has been a fast-developing topic in educational area. In 1999, Mayer put forward “Cognitive Theory of Multimedia learning” (Moreno, & Mayer, 1999). The theory consisted of several principles. One of the principles, Modality Principle describes that when learners are presented with spoken words, their performance are better than that with on-screen texts (Mayer, R., Dow, & Mayer, S. 2003; Moreno, & Mayer, 1999).It gave an implication that learners performance can be affected by modality of learning materials. A very common tool in education in literature and language is narrative. This way of storytelling has received success in practical use. The advantages of using narrative includes (a) inherent format advantage such as simple structure and familiar language and ideas, (b) motivating learners, (c) facilitate listening, (d) oral ability and (e)provide schema for comparison in comprehension. Although this storytelling method has been widely used in literature, language and even moral education, few studies focused it on science and technology area. The study aims to test the effect of narrative effect in multimedia setting with science topic. A script-based story was applied. The multimedia settings include a virtual human with synthetic speech, and animation on a solar cell lesson. The experiment design is a randomized alternative- treatments design, in which participants are requested to watch a video with pedagogical agent in story format or not. Participants were collected from Amazon Mechanical Turk. Result of transfer score and retention score showed that no significant difference between narrative and non-narrative condition. Discussion was put forward for future study.Dissertation/ThesisMasters Thesis Engineering 201

Using Natural Language as Knowledge Representation in an Intelligent Tutoring System

Knowledge used in an intelligent tutoring system to teach students is usually acquired from authors who are experts in the domain. A problem is that they cannot directly add and update knowledge if they don’t learn formal language used in the system. Using natural language to represent knowledge can allow authors to update knowledge easily. This thesis presents a new approach to use unconstrained natural language as knowledge representation for a physics tutoring system so that non-programmers can add knowledge without learning a new knowledge representation. This approach allows domain experts to add not only problem statements, but also background knowledge such as commonsense and domain knowledge including principles in natural language. Rather than translating into a formal language, natural language representation is directly used in inference so that domain experts can understand the internal process, detect knowledge bugs, and revise the knowledgebase easily. In authoring task studies with the new system based on this approach, it was shown that the size of added knowledge was small enough for a domain expert to add, and converged to near zero as more problems were added in one mental model test. After entering the no-new-knowledge state in the test, 5 out of 13 problems (38 percent) were automatically solved by the system without adding new knowledge

The Debate Over Understanding in AI's Large Language Models

We survey a current, heated debate in the AI research community on whether large pre-trained language models can be said to "understand" language -- and the physical and social situations language encodes -- in any important sense. We describe arguments that have been made for and against such understanding, and key questions for the broader sciences of intelligence that have arisen in light of these arguments. We contend that a new science of intelligence can be developed that will provide insight into distinct modes of understanding, their strengths and limitations, and the challenge of integrating diverse forms of cognition.Comment: Under submission as a Perspective articl