Learning and Measuring Specialization in Collaborative Swarm Systems

This paper addresses qualitative and quantitative diversity and specialization issues in the framework of selforganizing, distributed, artificial systems. Both diversity and specialization are obtained via distributed learning from initially homogeneous swarms. While measuring diversity essentially quantifies differences among the individuals, assessing the degree of specialization implies correlation between the swarm’s heterogeneity with its overall performance. Starting from the stick-pulling experiment in collective robotics, a task that requires the collaboration of two robots, we abstract and generalize in simulation the task constraints to k robots collaborating sequentially or in parallel. We investigate quantitatively the influence of task constraints and types of reinforcement signals on performance, diversity, and specialization in these collaborative experiments. Results show that, though diversity is not explicitly rewarded in our learning algorithm, even in scenarios without explicit communication among agents the swarm becomes specialized after learning. The degrees of both diversity and specialization are affected strongly by environmental conditions and task constraints. While the specialization measure reveals characteristics related to performance and learning in a clearer way than diversity does, the latter measure appears to be less sensitive to different noise conditions and learning parameters

Diversity and Specialization in Collaborative Swarm Systems

This paper addresses qualitative and quantitative diversity and specialization issues in the frame- work of self-organizing, distributed, artificial systems. Both diversity and specialization are obtained via distributed learning from initially homogeneous swarms. While measuring diversity essentially quantifies differences among the individuals, assessing the degree of specialization implies to correlate the swarm’s heterogeneity with its overall performance. Starting from a stick-pulling experiment in collective robotics, a task that requires the collaboration of two robots, we abstract and generalize in simulation the task constraints to k robots collaborating sequentially or in parallel. We investi- gate quantitatively the influence of task constraints and type of reinforcement signals on diversity and specialization in these collaborative experiments. Results show that, though diversity is not explicitly rewarded in our learning algorithm and there is no explicit communication among agents, the swarm becomes specialized after learning. The degree of specialization is affected strongly by environmental conditions and task constraints, and reveals characteristics related to performance and learning in a more consistent and clearer way than diversity does

Measuring the collective intelligence education index

War games and sports games always seek glory and excellence in an environment where participants enjoy what they do. Success is guaranteed in the degree of effective collaboration and coordination within the team members, as well as the strategy used by teams, such games or war strategies are generated since the birth of humanity. In this sense, the following questions emerge in the field of education: Is it possible to design learning activities that use this principle applied to collaborative work in the classroom? Which are the conditions of application of team competition strategy using ICT tools and how to measure it? This research explores the application of a web tool called Choose the Best (CTB). CTB implements a strategy that fosters competitiveness among the teams of a class, as well as the coordination and collaboration within the same, these types of strategies contribute to the development of Collective Intelligence levels. It's measured through a group of implemented metrics. Based on the results, we consider that the use of new forms of teaching and learning based on the emerging paradigms is necessary. Therefore, CTB is a tool that could become an effective way to measuring the group's performance according to Collective Intelligence paradigms.Postprint (author's final draft

Emergence of Diversity in a Group of Identical Bio-Robots

Learning capabilities, often guided by competition/cooperation, play a fundamental and ubiquitous role in living beings. Moreover, several behaviours, such as feeding and courtship, involve environmental exploration and exploitation, including local competition, and lead to a global benefit for the colony. This can be considered as a form of global cooperation, even if the individual agent is not aware of the overall effect. This paper aims to demonstrate that identical biorobots, endowed with simple neural controllers, can evolve diversified behaviours and roles when competing for the same resources in the same arena. These behaviours also produce a benefit in terms of time and energy spent by the whole group. The robots are tasked with a classical foraging task structured through the cyclic activation of resources. The result is that each individual robot, while competing to reach the maximum number of available targets, tends to prefer a specific sequence of subtasks. This indirectly leads to the global result of task partitioning, whereby the cumulative energy spent, in terms of the overall travelled distance and the time needed to complete the task, tends to be minimized. A series of simulation experiments is conducted using different numbers of robots and scenarios: the common emergent result obtained is the role specialization of each robot. The description of the neural controller and the specialization mechanisms are reported in detail and discussed

The science-of-team-science, transdisciplinary capacity, and shifting paradigms for translational professionals

The Science-of-Team-Science (SciTS) has become an important area of study as collaborative research becomes more normative throughout science inquiry and especially in medical and healthcare sectors. Team science aims for higher and collaborative levels of inquiry that operate within economies of knowledge similar to transdisciplinarity that strive to synthesize knowledge and innovate as a result of newly developed and hybridized methods of approach. This newly becoming and normalizing mode of science will require professionals to be aware of and embrace the shifting realities which have been the consequence of this new economy of knowledge. The next century of inquiry will require new generations of translational professionals that are keenly aware of their role as part of the translational process no matter what role they presently play in the continuum of bench to bedside to storefront healthcare. This paper reviews the SciTS landscape and theories of transdisciplinarity. It also provides insights about the shifting paradigms currently occurring in the discourse and identifies challenges for translational professionals

Swarm intelligence for clustering dynamic data sets for web usage mining and personalization.

Swarm Intelligence (SI) techniques were inspired by bee swarms, ant colonies, and most recently, bird flocks. Flock-based Swarm Intelligence (FSI) has several unique features, namely decentralized control, collaborative learning, high exploration ability, and inspiration from dynamic social behavior. Thus FSI offers a natural choice for modeling dynamic social data and solving problems in such domains. One particular case of dynamic social data is online/web usage data which is rich in information about user activities, interests and choices. This natural analogy between SI and social behavior is the main motivation for the topic of investigation in this dissertation, with a focus on Flock based systems which have not been well investigated for this purpose. More specifically, we investigate the use of flock-based SI to solve two related and challenging problems by developing algorithms that form critical building blocks of intelligent personalized websites, namely, (i) providing a better understanding of the online users and their activities or interests, for example using clustering techniques that can discover the groups that are hidden within the data; and (ii) reducing information overload by providing guidance to the users on websites and services, typically by using web personalization techniques, such as recommender systems. Recommender systems aim to recommend items that will be potentially liked by a user. To support a better understanding of the online user activities, we developed clustering algorithms that address two challenges of mining online usage data: the need for scalability to large data and the need to adapt cluster sing to dynamic data sets. To address the scalability challenge, we developed new clustering algorithms using a hybridization of traditional Flock-based clustering with faster K-Means based partitional clustering algorithms. We tested our algorithms on synthetic data, real VCI Machine Learning repository benchmark data, and a data set consisting of real Web user sessions. Having linear complexity with respect to the number of data records, the resulting algorithms are considerably faster than traditional Flock-based clustering (which has quadratic complexity). Moreover, our experiments demonstrate that scalability was gained without sacrificing quality. To address the challenge of adapting to dynamic data, we developed a dynamic clustering algorithm that can handle the following dynamic properties of online usage data: (1) New data records can be added at any time (example: a new user is added on the site); (2) Existing data records can be removed at any time. For example, an existing user of the site, who no longer subscribes to a service, or who is terminated because of violating policies; (3) New parts of existing records can arrive at any time or old parts of the existing data record can change. The user\u27s record can change as a result of additional activity such as purchasing new products, returning a product, rating new products, or modifying the existing rating of a product. We tested our dynamic clustering algorithm on synthetic dynamic data, and on a data set consisting of real online user ratings for movies. Our algorithm was shown to handle the dynamic nature of data without sacrificing quality compared to a traditional Flock-based clustering algorithm that is re-run from scratch with each change in the data. To support reducing online information overload, we developed a Flock-based recommender system to predict the interests of users, in particular focusing on collaborative filtering or social recommender systems. Our Flock-based recommender algorithm (FlockRecom) iteratively adjusts the position and speed of dynamic flocks of agents, such that each agent represents a user, on a visualization panel. Then it generates the top-n recommendations for a user based on the ratings of the users that are represented by its neighboring agents. Our recommendation system was tested on a real data set consisting of online user ratings for a set of jokes, and compared to traditional user-based Collaborative Filtering (CF). Our results demonstrated that our recommender system starts performing at the same level of quality as traditional CF, and then, with more iterations for exploration, surpasses CF\u27s recommendation quality, in terms of precision and recall. Another unique advantage of our recommendation system compared to traditional CF is its ability to generate more variety or diversity in the set of recommended items. Our contributions advance the state of the art in Flock-based 81 for clustering and making predictions in dynamic Web usage data, and therefore have an impact on improving the quality of online services

Specialization as an Optimal Strategy Under Varying External Conditions

We present an investigation of specialization when considering the execution of collaborative tasks by a robot swarm. Specifically, we consider the stick-pulling problem first proposed by Martinoli et al. [1], [2] and develop a macroscopic analytical model for the swarm executing a set of tasks that require the collaboration of two robots. We show, for constant external conditions, maximum productivity can be achieved by a single species swarm with carefully chosen operational parameters. While the same applies for a two species swarm, we show how specialization is a strategy best employed for changing external conditions

Management consulting skills: Towards an integrative matrix

Management consultant’s competencies have been a fragmented field of research where many proposals abound, but convergence is still pending. This research aims to offer such an integrative competency framework by critically analyzing extant models, conducting a documental content analysis to extract a list of competencies, which was subjected to empirical scrutiny via two different consensus seeking techniques: Swarm analysis and Delphi technique. Counting on three groups of participants from postgraduate studies, independent judgments were collected on the relative importance of competencies for management consultancy. These were contrasted to validate its scores and ranking, and interrater agreement indicators were applied to judge its convergence. Findings show that there is wide consensus and that a valid core competency framework to be used in management consultancy is deployable. Complementarily, findings prove that Swarm and Delphi mostly converge, and that Swarm analysis brings more efficiency to the process and opens up new analytical possibilities.As competências dos consultores de gestão têm sido um campo de investigação fragmentado onde muitas propostas abundam, porém, ainda sem que haja convergência entre si. Esta investigação visa encontrar um quadro integrador de competências através da análise crítica dos modelos existentes, conduzindo uma análise de conteúdo documental para extrair uma lista de competências, depois submetida a um escrutínio empírico através de duas técnicas diferentes de procura de consenso: a análise Swarm e a técnica Delphi. Contando com três grupos de participantes provindos de estudos pós-graduados, foram recolhidos julgamentos independentes sobre a importância relativa das competências para a consultoria de gestão. Estes foram contrastados para validar as suas pontuações e classificações, e foram usados indicadores de concordância para avaliar a sua convergência. As conclusões mostram que existe um amplo consenso e que é viável obter um quadro válido de competências centrais na consultoria de gestão. Complementarmente, os resultados mostram que o Swarm e o Delphi convergem fortemente, e que a análise Swarm traz mais eficiência ao processo e abre novas possibilidades analíticas