DCAART 2015 Abstracts


Full Papers
Paper Nr: 3
Title:

Computing with Perceptions for the Linguistic Description of Complex Phenomena through the Analysis of Time Series Data

Authors:

A. Ramos-Soto, A. Bugarín and S. Barro

Abstract: We are living in a world which is increasingly flooded with vast amounts of data. As a consequence, the use of techniques allowing to exploit and explain the information contained in this raw data has become mandatory. In this context, more human-friendly alternatives to standard techniques like statistics or data mining approaches are being considered. Among them, the soft computing field provides a set of tools allowing the creation of linguistic descriptions of data. These are automatically generated textual explanations that comprise the most relevant information that is implicit in the data, providing linguistic concepts which deal with the imprecision and ambiguity of language through the use of fuzzy sets. Following this research line, the Ph.D. we propose explores the potential of this field by providing real solutions employing linguistic descriptions and also extending the current theoretical base to consider a higher expressiveness.

Paper Nr: 5
Title:

Disentangling Cognitive and Constructivist Aspects of Hierarchies

Authors:

Stefano Bennati

Abstract: One of the most puzzling problems in the social sciences is the emergence of social institutions. The field of sociology is trying to understand why our society is the way we know it and whether an alternative, possibly better, society would be possible. One of the fundamental questions is the emergence of hierarchies. The cognitive approach suggests that hierarchies are encoded in human nature, therefore are the most natural form of organization; on the other hand the costructivist approach sees hierarchies as a product of interactions between individuals that emerges independently of individual preferences. We will investigate under which conditions hierarchies emerge from a cognitive factor, a constructivist factor or a combination of both. We will study this question both at the analytic level, with the help of Agent-Based simulations where agents are Neural Networks, and at the empirical level by running sociological experiments in our laboratory.

Paper Nr: 6
Title:

A New Approach for the Detection of Emergent Behaviors and Implied Scenarios in Distributed Software Systems - Extracting Communications from Scenarios

Authors:

Fatemeh Hendijani Fard and Behrouz H. Far

Abstract: An approach to specify the requirements and design of a Distributed Software System (DSS), which is mostly used in recent years, is describing scenarios with visual artifacts, such as, UML Sequence Diagrams and ITU-T Message Sequence Charts (MSC) and High level Message Sequence Charts (hMSC). Scenarios describe system’s behavior and define the components and their interactions. Each scenario determines a partial behavior of the system. Hence, the restricted view of the components in each scenario and distributed functionality and/or control in DSS, may result in inconsistency in the system behavior. One problem that arise in scenario based Distributed Software Systems is emergent behaviors or implied scenarios that occur because of restricted view of one or more components. Emergent behaviors are known as unexpected behaviors that components show in their execution time. However, this behavior was not defined in their designs. This unexpected behavior may imply a new scenario to the system, and can result in considerable cost and damage. Therefore, emergent behaviors should be detected in the early phases of software development to prevent damage or cost after deployment. The detected emergent behaviors can be either accepted or denied by the stakeholders. However, they should be detected and discussed, to be added as new designs, or to be specified as negative scenarios that should be avoided. In our research, we try to devise an automatic methodology to detect the emergent behaviors (implied scenarios) from the designs of the system. We also mean to help the designers for the exact point of the problem in the system and the possible solutions to remove the detected emergent behaviors.

Paper Nr: 8
Title:

Alternative Approaches to Planning

Authors:

Otakar Trunda

Abstract: In my PhD. dissertation, I focus on action planning and constrained discrete optimization. I try to introduce novel approaches to the field of single-agent planning by combining standard techniques with meta-heuristic optimization, machine-learning algorithms, hyper-euristics and algorithm selection approaches. Our main goal is to create new and flexible planning algorithms which would be suited for a large variety of real-life problems. Planning is a fundamental and difficult problem in AI and any new results in this area are directly applicable to many other fields. They can be used for single-agent or multi-agent action selection in both competitive or cooperative environment and as we focus on optimization, our techniques are suitable for real-life problems that arise in robotics or transportation.

Paper Nr: 9
Title:

Automatic Generation of Learning Path

Authors:

Claudia Perez-Martinez, Gabriel Lopez Morteo, Magally Martinez Reyes and Alexander Gelbukh

Abstract: This paper presents a proposal to automatically generate a learning path. The proposal method apply Natural Language Processing techniques, it uses as knowledge source an ontological view from Wikipedia, taking advantage of its broad domain of concepts. The results has been validated comparing them with the teaching opinion. It is expected that the learning path built can be an useful input to instructional design processes considering them before to know the student profile.

Paper Nr: 10
Title:

Measuring Intrinsic Quality of Human Decisions

Authors:

Tamal T. Biswas

Abstract: Research on judging decisions made by fallible (human) agents is not as much advanced as research on finding optimal decisions, and on supervision of AI agents´ decisions by humans. Human decisions are often influenced by various factors, such as risk, uncertainty, time pressure, and depth of cognitive capability, whereas decisions by an AI agent can be effectively optimal without these limitations. The concept of `depth´, a well-defined term in game theory (including chess), does not have a clear formulation in decision theory. To quantify ´depth´ in decision theory, we can configure an AI agent of supreme competence to `think´ at depths beyond the capability of any human, and in the process collect evaluations of decisions at various depths. One research goal is to create an intrinsic measure of the depth of thinking required to answer certain test questions, toward a reliable means of assessing their difficulty apart from item-response statistics. We relate the depth of cognition by humans to depths of search, and use this information to infer the quality of decisions made, so as to judge the decision-maker from his decisions. Our research extends the model of Regan and Haworth to quantify depth, plus related measures of complexity and difficulty, in the context of chess. We use large data from real chess tournaments and evaluations from chess programs (AI agents) of strength beyond all human players. We then seek to transfer the results to other decision-making fields in which effectively optimal judgements can be obtained from either hindsight, answer banks, or powerful AI agents. In some applications, such as multiple-choice tests, we establish an isomorphism of the underlying mathematical quantities, which induces a correspondence between various measurement theories and the chess model. We provide results toward the objective of applying the correspondence in reverse to obtain and quantify measure of depth and difficulty for multiple-choice tests, stock market trading, and other real-world applications.