In this paper, we investigate the profit-aware team grouping problem in social networks. We consider a setting in which people possess different skills and compatibility among these individuals is captured by a social network. Here, we assume a collection of tasks, where each task requires a specific set of skills, and yields a different profit upon completion. Active and qualified individuals may collaborate with each other in the form of to accomplish a set of tasks. Our goal is to find a grouping method that maximizes the total profit of the tasks that these teams can complete. Any feasible grouping must satisfy the following three conditions: (i) each team possesses all skills required by the task, (ii) individuals within the same team are social compatible, and (iii) each individual is not overloaded. We refer to this as the problem. Our work presents a detailed analysis of the computational complexity of the problem, and propose a LP-based approximation algorithm to tackle it and its variants. Although we focus on team grouping in this paper, our results apply to a broad range of optimization problems that can be formulated as a cover decomposition problem.
To the best of our knowledge we are the first to formulate and study the team grouping problem and its variants. However, our work is closely related to other team formation and cluster hiring problems. @cite_0 introduce the minimum cost team formation problem. Given a set of skills that need to be covered and social network, their objective is to select a team of experts that can cover all required skills, while ensuring efficient communication between the team's members. There is a considerable amount of literature on this topic and its variants . In , they study cluster hire problem, where the objective is to hire a profit-maximizing team of experts with the ability to complete multiple projects, subject to a fixed budget. Different from all the above works where they aim to select a best qualified team, our objective is to group individuals into multiple teams. It turns out that these two problems are closely related, this allows us to leverage existing techniques for team formation to solve our problem.
@cite_0
2097645701 2091149117 2141157184 2583316528
This paper introduces the problem of combining multiple partitionings of a set of objects into a single consolidated clustering without accessing the features or algorithms that determined these partitionings. We first identify several application scenarios for the resultant 'knowledge reuse' framework that we call cluster ensembles. The cluster ensemble problem is then formalized as a combinatorial optimization problem in terms of shared mutual information. In addition to a direct maximization approach, we propose three effective and efficient techniques for obtaining high-quality combiners (consensus functions). The first combiner induces a similarity measure from the partitionings and then reclusters the objects. The second combiner is based on hypergraph partitioning. The third one collapses groups of clusters into meta-clusters which then compete for each object to determine the combined clustering. Due to the low computational costs of our techniques, it is quite feasible to use a supra-consensus function that evaluates all three approaches against the objective function and picks the best solution for a given situation. We evaluate the effectiveness of cluster ensembles in three qualitatively different application scenarios: (i) where the original clusters were formed based on non-identical sets of features, (ii) where the original clustering algorithms worked on non-identical sets of objects, and (iii) where a common data-set is used and the main purpose of combining multiple clusterings is to improve the quality and robustness of the solution. Promising results are obtained in all three situations for synthetic as well as real data-sets. Previous approaches to select agents to form a team rely on single-agent capabilities, and team performance is treated as a sum of such known capabilities. Motivated by complex team formation situations, we address the problem where both single-agent capabilities may not be known upfront, e.g., as in ad hoc teams, and where team performance goes beyond single-agent capabilities and depends on the specific synergy among agents. We formally introduce a novel weighted synergy graph model to capture new interactions among agents. Agents are represented as vertices in the graph, and their capabilities are represented as Normally-distributed variables. The edges of the weighted graph represent how well the agents work together, i.e., their synergy in a team. We contribute a learning algorithm that learns the weighted synergy graph using observations of performance of teams of only two and three agents. Further, we contribute two team formation algorithms, one that finds the optimal team in exponential time, and one that approximates the optimal team in polynomial time. We extensively evaluate our learning algorithm, and demonstrate the expressiveness of the weighted synergy graph in a variety of problems. We show our approach in a rich ad hoc team formation problem capturing a rescue domain, namely the RoboCup Rescue domain, where simulated robots rescue civilians and put out fires in a simulated urban disaster. We show that the weighted synergy graph outperforms a competing algorithm, thus illustrating the efficacy of our model and algorithms. In the real world, noisy sensors and limited communication make it difficult for robot teams to coordinate in tightly coupled tasks. Team members cannot simply apply single-robot solution techniques for partially observable problems in parallel because they do not take into account the recursive effect that reasoning about the beliefs of others has on policy generation. Instead, we must turn to a game theoretic approach to model the problem correctly. Partially observable stochastic games (POSGs) provide a solution model for decentralized robot teams, however, this model quickly becomes intractable. In previous work we presented an algorithm for lookahead search in POSGs. Here we present an extension which reduces computation during lookahead by clustering similar observation histories together. We show that by clustering histories which have similar profiles of predicted reward, we can greatly reduce the computation time required to solve a POSG while maintaining a good approximation to the optimal policy. We demonstrate the power of the clustering algorithm in a real-time robot controller as well as for a simple benchmark problem. Professional career training for novice employees at elementary levels to help them master necessary working skills is critical for both achieving employees' professional success and enhancing the enterprise growth. Besides adopting professional services from external career training agencies, companies can actually train the employees more effectively by involving them in various internal projects carried out in the companies. In this paper, we will study the "Employee Training" (ET) problem by assigning the employees to various concrete company internal projects. From the company perspective, besides training the employees, another important objective of carrying out these projects is to finish them successfully. The successful accomplishment of projects depends on various issues, like the skill qualification of the built teams and the effective collaboration among the team members. To achieve these two objectives simultaneously, a novel framework named "Team foRmAtion based employee traINing" (TRAIN) is proposed in this paper. TRAIN formulates the ET problem as a joint optimization problem, where the objective function considers the employees' overall skill gain and the team internal communication costs at the same time. To ensure the success of the projects, a new team skill qualification constraint is proposed and added to the optimization problem. Extensive experiments conducted on the real-world enterprise employee project team dataset demonstrate the effectiveness of TRAIN in addressing the problem.