Synergistic Team Composition

Effective teams are crucial for organisations, especially in environments that require teams to be constantly created and dismantled, such as software development, scientific experiments, crowd-sourcing, or the classroom. Key factors influencing team performance are competences and personality of team members. Hence, we present a computational model to compose proficient and congenial teams based on individuals' personalities and their competences to perform tasks of different nature. With this purpose, we extend Wilde's post-Jungian method for team composition, which solely employs individuals' personalities. The aim of this study is to create a model to partition agents into teams that are balanced in competences, personality and gender. Finally, we present some preliminary empirical results that we obtained when analysing student performance. Results show the benefits of a more informed team composition that exploits individuals' competences besides information about their personalities

Exploiting Heterogeneity in Networks of Aerial and Ground Robotic Agents

By taking advantage of complementary communication technologies, distinct sensing functionalities and varied motion dynamics present in a heterogeneous multi-robotic network, it is possible to accomplish a main mission objective by assigning specialized sub-tasks to specific members of a robotic team. An adequate selection of the team members and an effective coordination are some of the challenges to fully exploit the unique capabilities that these types of systems can offer. Motivated by real world applications, we focus on a multi-robotic network consisting off aerial and ground agents which has the potential to provide critical support to humans in complex settings. For instance, aerial robotic relays are capable of transporting small ground mobile sensors to expand the communication range and the situational awareness of first responders in hazardous environments. In the first part of this dissertation, we extend work on manipulation of cable-suspended loads using aerial robots by solving the problem of lifting the cable-suspended load from the ground before proceeding to transport it. Since the suspended load-quadrotor system experiences switching conditions during this critical maneuver, we define a hybrid system and show that it is differentially-flat. This property facilitates the design of a nonlinear controller which tracks a waypoint-based trajectory associated with the discrete states of the hybrid system. In addition, we address the case of unknown payload mass by combining a least-squares estimation method with the designed controller. Second, we focus on the coordination of a heterogeneous team formed by a group of ground mobile sensors and a flying communication router which is deployed to sense areas of interest in a cluttered environment. Using potential field methods, we propose a controller for the coordinated mobility of the team to guarantee inter-robot and obstacle collision avoidance as well as connectivity maintenance among the ground agents while the main goal of sensing is carried out. For the case of the aerial communications relays, we combine antenna diversity with reinforcement learning to dynamically re-locate these relays so that the received signal strength is maintained above a desired threshold. Motivated by the recent interest of combining radio frequency and optical wireless communications, we envision the implementation of an optical link between micro-scale aerial and ground robots. This type of link requires maintaining a sufficient relative transmitter-receiver position for reliable communications. In the third part of this thesis, we tackle this problem. Based on the link model, we define a connectivity cone where a minimum transmission rate is guaranteed. For example, the aerial robot has to track the ground vehicle to stay inside this cone. The control must be robust to noisy measurements. Thus, we use particle filters to obtain a better estimation of the receiver position and we design a control algorithm for the flying robot to enhance the transmission rate. Also, we consider the problem of pairing a ground sensor with an aerial vehicle, both equipped with a hybrid radio-frequency/optical wireless communication system. A challenge is positioning the flying robot within optical range when the sensor location is unknown. Thus, we take advantage of the hybrid communication scheme by developing a control strategy that uses the radio signal to guide the aerial platform to the ground sensor. Once the optical-based signal strength has achieved a certain threshold, the robot hovers within optical range. Finally, we investigate the problem of building an alliance of agents with different skills in order to satisfy the requirements imposed by a given task. We find this alliance, known also as a coalition, by using a bipartite graph in which edges represent the relation between agent capabilities and required resources for task execution. Using this graph, we build a coalition whose total capability resources can satisfy the task resource requirements. Also, we study the heterogeneity of the formed coalition to analyze how it is affected for instance by the amount of capability resources present in the agents

Sequences of coalition structures in multi-agent systems applied to disaster response

Die Koalitionsbildung ist ein interessantes Thema im Bereich der Multiagentensysteme aufgrund von Herausforderungen bei der praktischen Anwendung, sowie der Komplexität der Berechnung von Lösungen des Problems. Eine Koalition ist ein kurzlebiger Zusammenschluss von Agenten, die ein gemeinsames Ziel verfolgen. Gleichzeitig bietet die kooperative Spieltheorie mit Koalitionen einen formalen Mechanismus zur Analyse von Gruppen aus verschiedenen Akteuren. Daher wird das Problem als Characteristic-Function Game (CFG) modelliert. Dessen Ergebnis sind Aufteilungen einer Menge von Agenten in Koalitionen, sogenannte Koalitionsstrukturen. Allerdings lassen sich nicht alle praktisch auftretenden Probleme effizient mit einer einzigen Koalitionsstruktur lösen. Beispielsweise kann es erforderlich sein, eine Hierarchie von Gruppen zu bilden, in der dann eine Koalitionsstruktur pro Ebene benötigt wird. In der vorliegenden Arbeit werden voneinander abhängige Probleme der Koalitionsbildung untersucht. Insbesondere wird der Schwerpunkt auf die gegenseitige Abhängigkeit von Lösungen (also Koalitionsstrukturen), die aus individuellen Spielen resultieren, gelegt. Angesichts des Mangels an wissenschaftlichen Arbeiten zu diesem Thema wird das Sequential Characteristic-Function Game (SCFG) vorgeschlagen, um die Beziehung zwischen aufeinanderfolgenden Koalitionsstrukturen als Folge von CFGs zu modellieren. Dieses neue Spiel wird erweitert, um spezifische Beschränkungen für jedes CFG in der Spielsequenz zu ermöglichen. Darüber hinaus wird gezeigt, dass das zugrunde liegende SCFG-Problem PSPACE-vollständig ist. Es werden ein exakter Algorithmus zur Berechnung von Lösungen von SCFG-Instanzen, sowie zwei heuristische Algorithmen vorgeschlagen. Die letzte Herausforderung der vorliegenden Arbeit ist die Modellierung eines Katastrophenhilfseinsatzes, bei dem das Einsatzleitsystem (engl. Incident Command System) verwendet wird, mithilfe der vorgeschlagenen Techniken und Algorithmen.Coalition formation has long been an interesting topic of research in Multi-Agent Systems, either for its practical applications or complexity issues. A coalition is commonly understood as a short-lived and goal-directed structure, in which the agents join forces to achieve a goal. Cooperative game theory has been used as a formal mechanism to analyse the problem of grouping agents into coalitions. The problem is then modelled by a Characteristic-Function Game (CFG) in which the outcome is a coalition structure: a partition of agents into coalitions. However, not all problems can be efficiently solved using a single coalition structure. For instance, one might be interested in a group hierarchy in which a coalition structure per level is required. In this thesis, we investigate coalition formation problems that are interdependent. In particular, we focus on the interdependence among solutions (i.e., coalition structures) produced by each game individually. Given the lack of work on this topic, we propose a novel game named Sequential Characteristic-Function Game (SCFG), which aims to model the relationships between subsequent coalition structures in a sequence of CFGs. We approach the resulting problem under both theoretical and practical perspectives. We extend the proposed game to allow fine-grained constraints being induced over each CFG in the sequence. Also, we show that the underlying SCFG problem is PSPACE-complete. From an algorithmic viewpoint, we propose an exact algorithm based on dynamic programming, as well as two heuristic algorithms to compute solutions for SCFG instances. We show that there exists a trade-off in choosing one algorithm over the others. Moreover, we model a disaster response operation that employs the incident command system framework, and we show how one can apply our proposed framework and algorithms to solve such an interesting problem

Eminence Grise Coalitions: On the Shaping of Public Opinion

We consider a network of evolving opinions. It includes multiple individuals with first-order opinion dynamics defined in continuous time and evolving based on a general exogenously defined time-varying underlying graph. In such a network, for an arbitrary fixed initial time, a subset of individuals forms an eminence grise coalition, abbreviated as EGC, if the individuals in that subset are capable of leading the entire network to agreeing on any desired opinion, through a cooperative choice of their own initial opinions. In this endeavor, the coalition members are assumed to have access to full profile of the underlying graph of the network as well as the initial opinions of all other individuals. While the complete coalition of individuals always qualifies as an EGC, we establish the existence of a minimum size EGC for an arbitrary time-varying network; also, we develop a non-trivial set of upper and lower bounds on that size. As a result, we show that, even when the underlying graph does not guarantee convergence to a global or multiple consensus, a generally restricted coalition of agents can steer public opinion towards a desired global consensus without affecting any of the predefined graph interactions, provided they can cooperatively adjust their own initial opinions. Geometric insights into the structure of EGC's are given. The results are also extended to the discrete time case where the relation with Decomposition-Separation Theorem is also made explicit.Comment: 35 page

Scaling reinforcement learning to the unconstrained multi-agent domain

Reinforcement learning is a machine learning technique designed to mimic the way animals learn by receiving rewards and punishment. It is designed to train intelligent agents when very little is known about the agent’s environment, and consequently the agent’s designer is unable to hand-craft an appropriate policy. Using reinforcement learning, the agent’s designer can merely give reward to the agent when it does something right, and the algorithm will craft an appropriate policy automatically. In many situations it is desirable to use this technique to train systems of agents (for example, to train robots to play RoboCup soccer in a coordinated fashion). Unfortunately, several significant computational issues occur when using this technique to train systems of agents. This dissertation introduces a suite of techniques that overcome many of these difficulties in various common situations. First, we show how multi-agent reinforcement learning can be made more tractable by forming coalitions out of the agents, and training each coalition separately. Coalitions are formed by using information-theoretic techniques, and we find that by using a coalition-based approach, the computational complexity of reinforcement-learning can be made linear in the total system agent count. Next we look at ways to integrate domain knowledge into the reinforcement learning process, and how this can signifi-cantly improve the policy quality in multi-agent situations. Specifically, we find that integrating domain knowledge into a reinforcement learning process can overcome training data deficiencies and allow the learner to converge to acceptable solutions when lack of training data would have prevented such convergence without domain knowledge. We then show how to train policies over continuous action spaces, which can reduce problem complexity for domains that require continuous action spaces (analog controllers) by eliminating the need to finely discretize the action space. Finally, we look at ways to perform reinforcement learning on modern GPUs and show how by doing this we can tackle significantly larger problems. We find that by offloading some of the RL computation to the GPU, we can achieve almost a 4.5 speedup factor in the total training process

Virtual power producers integration into MASCEM

All over the world Distributed Generation is seen as a valuable help to get cleaner and more efficient electricity. Under this context distributed generators, owned by different decentralized players can provide a significant amount of the electricity generation. To get negotiation power and advantages of scale economy, these players can be aggregated giving place to a new concept: the Virtual Power Producer. Virtual Power Producers are multi-technology and multi-site heterogeneous entities. Virtual Power Producers should adopt organization and management methodologies so that they can make Distributed Generation a really profitable activity, able to participate in the market. In this paper we address the integration of Virtual Power Producers into an electricity market simulator –MASCEM – as a coalition of distributed producers