Research Areas

  • Computational Sustainability

Computational Sustainability is a vast concern, or should be, and presents challenges stemming from interactions between the natural and human-developed spheres across temporal and spatial scales. This has motivated Computer Science researchers to apply their trade to environmental and societal sustainability challenges. Indeed, interdisciplinary multi-investigator research teams are focusing on cross-cutting issues such as Psychology, Economics, Mathematical Logic, Optimization, Dynamic Modelling, Big Data, Machine Learning and Citizen Science. These problem-solving methods and methodologies are applied to computational sustainable challenges, including Health, Nature Conservation, Poverty Reduction, Renewable Energy, just to name a few. Undeniably, the ability of Computational Sustainability in terms of planning and search technologies to consider many possible outcomes is a cognitive capability that would greatly benefit human problem solving and decision-making. In particular, the motivation and ability to explore the space of consequences of technology and policy interventions is little studied, but unanticipated consequences are not, necessarily, anticipatable consequences.

  • Artificial Intelligence

One's mission is to ensure that Artificial Intelligence (AI) - meaning highly autonomous systems that outperform humans in doing the most economically valuable work - benefits all of humanity. We will try to develop safe and useful AI directly, but also consider our mission accomplished if our work helps others achieve this result.

  • Quantum Computation

Quantum Computation is about building and controlling a “programmable molecule,” then using it to simulate and ask questions of nature - Marissa Giustina, Google Quantum Electronics Engineer - a team of Google researchers reached a milestone (quantum supremacy) building a quantum computer that needed only 3 minutes 20 seconds to perform a calculation that today’s computers couldn’t finish in 10,000 years.

  • A Thermodynamics Approach to Knowledge Representation and Reasoning

The main principles of thermodynamics can be summarized in one sentence, viz.

The total energy content of the universe is constant, and the total entropy increases steadily

Energy cannot be generated or destroyed, but it can be converted from one form to another. Every time it is transformed, there is a penalty - a loss of available energy for future work. In this article, we show one way to assess entropy in a nursing service using the example of professional satisfaction of nurses in an ICU. We consider the entropy values in the range 0…1, with zero (0) corresponding to an outstanding system state and one to system disruption or a chaos state.

Indeed, the problem-solving methodology presented in this article is based on The Laws of Thermodynamics and aims to describe the practices of Knowledge Representation and Reasoning (KRR) as a process of energy degradation. In order to explain the basic rules of the proposed approach, the First and Second Law of Thermodynamics are considered, attending that one`s system moves from state to state over time. The former one, also known as the Energy Saving Law, states that the total energy of an isolated system is constant, i.e., cannot change over time. This means that energy can be converted but cannot be generated or destroyed. The latter deals with Entropy, a property that quantifies the orderly state of a system and its evolution, i.e., that all energy in an isolated system passes from an ordered to a disordered state. These characteristics fit the proposed vision of KRR practices, as this has to be understood as a process of energy degradation, i.e., energy can be decomposed and used in sense of devaluation, but never used in the sense of destruction, viz.

exergy, sometimes called available energy or more precisely available work, is that part of the energy that can be arbitrarily used by a system, or in other words, giving a measure of its entropy;

vagueness, i.e., the corresponding energy values that may or may not have been consumed; and

anergy, that stands for an energetic potential that was not yet consumed, being therefore available, i.e., all of energy that is not exergy.

  • Logic Programming

On the other hand, there are many approaches to KRR using the epitome of Logic Programming (LP), namely in the areas of Model Theory and Proof Theory. In this article, the Proof Theoretical approach to problem solving was adopted and expressed as an extension of the LP language. Under this setting a LP will be grounded on a finite set of clauses in the form, viz.

Program - A Prime Instance of a Logic Program

The first clause denotes predicate’s closure, “,” designates “logical and”, while “?” is a domain atom denoting “falsity”, the pi, qj, and p are classical ground literals, i.e., either positive atoms or atoms preceded by the classical negation sign Ø . Indeed, Ø stands for strong negation, while not denotes negation-by-failure, i.e., a failure in proving a certain statement since it was not declared in an explicit way. According to this way of thinking, a set of abducibles are present in every program. In this work are given in the form of exceptions to the extensions of the predicates that make the program, i.e., clauses of the form, viz.

that denote data, information or knowledge that cannot be ruled out. On the other hand, clauses of the type, viz.

are invariants that make it possible to specify the context under which the universe of discourse should be understood.