In this paper we are proposing an approach for coordinating a team of homogeneous agents based on a flexible common Team Strategy as well as on the concepts of Situation Based Strategic Positioning and Dynamic Positioning and Role Exchange. We also introduce an Agent Architecture including a specific high-level decision module capable of implementing this strategy. Our proposal is based on the formalization of what is a team strategy for competing with an opponent team having opposite goals. A team strategy is composed of a set of agent types and a set of tactics, which are also composed of several formations. Formations are used for different situations and assign each agent a default spatial positioning and an agent type (defining its behaviour at several levels). Agent’s reactivity is also introduced for appropriate response to the dynamics of the current situation. However, in our approach this is done in a way that preserves team coherence instead of permitting uncoordinated agent behaviour. We have applied, with success, this coordination approach to the RoboSoccer simulated domain. The FC Portugal team, developed using this approach won the RoboCup2000 (simulation league) European and World championships scoring a total of 180 goals and conceding none.

This article focuses on evaluating the usability of an intelligent wheelchair (IW) in both real and simulated environments. The wheelchair is controlled at a high-level by a flexible multimodal interface, using voice commands, facial expressions, head movements and joystick as its main inputs. A quasi-experimental design was applied including a deterministic sample with a questionnaire that enabled to apply the system usability scale. The subjects were divided in two independent samples: 46 individuals performing the experiment with an IW in a simulated environment (28 using different commands in a sequential way and 18 with the liberty to choose the command); 12 individuals performing the experiment with a real IW The main conclusion achieved by this study is that the usability of the IW in a real environment is higher than in the simulated environment. However, there were not statistical evidences to affirm that there are differences between the real and simulated wheelchairs in terms of safety and control. Also, most of users considered the multimodal way of driving the wheelchair very practical and satisfactory. Thus, it may be concluded that the multimodal interfaces enables very easy and safe control of the IW both in simulated and real environments.

In general, games pose interesting and complex problems for the implementation of intelligent agents and are a popular domain in the study of artificial intelligence. In fact, games have been at the center of some of the most well-known achievements in artificial intelligence. From classical board games such as chess, checkers, backgammon and Go, to video games such as Dota 2 and StarCraft II, artificial intelligence research has devised computer programs that can play at the level of a human master and even at a human world champion level. Planning and learning, two well-known and successful paradigms of artificial intelligence, have greatly contributed to these achievements. Although representing distinct approaches, planning and learning try to solve similar problems and share some similarities. They can even complement each other. This has led to research on methodologies to combine the strengths of both approaches to derive better solutions. This paper presents a survey of the multiple methodologies that have been proposed to integrate planning and learning in the context of games. In order to provide a richer contextualization, the paper also presents learning and planning techniques commonly used in games, both in terms of their theoretical foundations and applications.