Artificial Intelligence and Systems Theory: Applied to Cooperative Robots

This paper describes an approach to the design of a population of cooperative robots based on concepts borrowed from Systems Theory and Artificial Intelligence. The research has been developed under the SocRob project, carried out by the Intelligent Systems Laboratory at the Institute for Systems and Robotics - Instituto Superior Tecnico (ISR/IST) in Lisbon. The acronym of the project stands both for "Society of Robots" and "Soccer Robots", the case study where we are testing our population of robots. Designing soccer robots is a very challenging problem, where the robots must act not only to shoot a ball towards the goal, but also to detect and avoid static (walls, stopped robots) and dynamic (moving robots) obstacles. Furthermore, they must cooperate to defeat an opposing team. Our past and current research in soccer robotics includes cooperative sensor fusion for world modeling, object recognition and tracking, robot navigation, multi-robot distributed task planning and coordination, including cooperative reinforcement learning in cooperative and adversarial environments, and behavior-based architectures for real time task execution of cooperating robot teams

Predicting Human Cooperation

The Prisoner's Dilemma has been a subject of extensive research due to its importance in understanding the ever-present tension between individual self-interest and social benefit. A strictly dominant strategy in a Prisoner's Dilemma (defection), when played by both players, is mutually harmful. Repetition of the Prisoner's Dilemma can give rise to cooperation as an equilibrium, but defection is as well, and this ambiguity is difficult to resolve. The numerous behavioral experiments investigating the Prisoner's Dilemma highlight that players often cooperate, but the level of cooperation varies significantly with the specifics of the experimental predicament. We present the first computational model of human behavior in repeated Prisoner's Dilemma games that unifies the diversity of experimental observations in a systematic and quantitatively reliable manner. Our model relies on data we integrated from many experiments, comprising 168,386 individual decisions. The computational model is composed of two pieces: the first predicts the first-period action using solely the structural game parameters, while the second predicts dynamic actions using both game parameters and history of play. Our model is extremely successful not merely at fitting the data, but in predicting behavior at multiple scales in experimental designs not used for calibration, using only information about the game structure. We demonstrate the power of our approach through a simulation analysis revealing how to best promote human cooperation.Comment: Added references. New inline citation style. Added small portions of text. Re-compiled Rmarkdown file with updated ggplot2 so small aesthetic changes to plot

The Principal Internship: How Can We Get It Right?

Examines educational leadership degree programs in the SREB region. Focuses on the problems within internships, and provides ideas on how programs can be designed to produce good school leaders

Cooperation Enforcement and Collusion Resistance in Repeated Public Goods Games

Enforcing cooperation among substantial agents is one of the main objectives for multi-agent systems. However, due to the existence of inherent social dilemmas in many scenarios, the free-rider problem may arise during agents' long-run interactions and things become even severer when self-interested agents work in collusion with each other to get extra benefits. It is commonly accepted that in such social dilemmas, there exists no simple strategy for an agent whereby she can simultaneously manipulate on the utility of each of her opponents and further promote mutual cooperation among all agents. Here, we show that such strategies do exist. Under the conventional repeated public goods game, we novelly identify them and find that, when confronted with such strategies, a single opponent can maximize his utility only via global cooperation and any colluding alliance cannot get the upper hand. Since a full cooperation is individually optimal for any single opponent, a stable cooperation among all players can be achieved. Moreover, we experimentally show that these strategies can still promote cooperation even when the opponents are both self-learning and collusive

Response to Privacy as a Public Good

In the spirit of moving forward the theoretical and empirical scholarship on privacy as a public good, this response addresses four issues raised by Professors Fairfield and Engel’s article: first, their depiction of individuals in groups; second, suggestions for clarifying the concept of group; third, an explanation of why the platforms on which groups exist and interact needs more analysis; and finally, the question of what kind of government intervention might be necessary to protect privacy as a public good

Education for citizenship: measuring the impact on learners of the community-based learning program in Palestine

The community-based learning (CBL) methodology was introduced at An-Najah University, Palestine for the first time through an initiative led by the Center for Excellence in Learning in 2013. The initial objectives for the CBL scheme were set at three different, yet interrelated aspects. On one hand, the learning environment was expanded to include direct engagement with the Palestinian community organizations through implementing need based projects for these organizations. On the other hand, through such engagement the learners were expected to develop key critical thinking skills which included self-learning, decision making, and testing theoretical models as they relate to community problems. Additionally, and as a direct impact for this initiative, it was hoped that the community work will prepare the learners for their responsibilities as Palestinian citizens. This research project is intended to measure the direct impact that the CBL program had on the learners’ skills on all three levels. This will be done by interviewing a representative sample from CBL participant groups. To measure the indirect impact on the CBL participants, the research will report on any unanticipated outcomes resulting from the CBL experience. In other words, this research will highlight the snowballing effect for the CBL program – aspects of growth in the learners experience beyond the originally planned objectives