Design concepts for the development of cooperative problem-solving systems

There are many problem-solving tasks that are too complex to fully automate given the current state of technology. Nevertheless, significant improvements in overall system performance could result from the introduction of well-designed computer aids. We have been studying the development of cognitive tools for one such problem-solving task, enroute flight path planning for commercial airlines. Our goal was two-fold. First, we were developing specific systems designs to help with this important practical problem. Second, we are using this context to explore general design concepts to guide in the development of cooperative problem-solving systems. These designs concepts are described

The Impact of Task- and Team-Generic Teamwork Skills Training on Team Effectiveness

This study examined the effects of training team members in three task- and teamgeneric teamwork skills: planning and task coordination, collaborative problem solving, and communication. We first examined the degree to which task- and team-generic teamwork skills training impacted team performance on a task unrelated to the content of the training program.We then examined whether the effects of task- and team-generic teamwork skills training on team performance were due to the transfer of skills directly related to planning and task coordination, collaborative problem solving, and communication. Results from 65 four-person project teams indicated that task- and team-generic teamwork skills training led to significantly higher levels of team performance. Results also indicated that the effects of task- and teamgeneric teamwork skills training on team performance were mediated by planning and task coordination and collaborative problem solving behavior. Although communication was positively affected by the task- and team-generic teamwork skills training, it did not mediate the relationship between task- and team-generic teamwork skills training and team performance.Theoretical and practical implications of these results are discussed, as well as possible limitations and directions for future research

On Using Unsatisfiability for Solving Maximum Satisfiability

Maximum Satisfiability (MaxSAT) is a well-known optimization pro- blem, with several practical applications. The most widely known MAXS AT algorithms are ineffective at solving hard problems instances from practical application domains. Recent work proposed using efficient Boolean Satisfiability (SAT) solvers for solving the MaxSAT problem, based on identifying and eliminating unsatisfiable subformulas. However, these algorithms do not scale in practice. This paper analyzes existing MaxSAT algorithms based on unsatisfiable subformula identification. Moreover, the paper proposes a number of key optimizations to these MaxSAT algorithms and a new alternative algorithm. The proposed optimizations and the new algorithm provide significant performance improvements on MaxSAT instances from practical applications. Moreover, the efficiency of the new generation of unsatisfiability-based MaxSAT solvers becomes effectively indexed to the ability of modern SAT solvers to proving unsatisfiability and identifying unsatisfiable subformulas

Group Capabilities and Process Quality in Complex Problem Solving

During my (lean) consultancy assignments I experienced that many organisations and groups struggle with achieving improvement results, not having an effective process of problem solving. In my PhD research, I therefore explored the quality of groups’ problem-solving processes. In this research I used mixed-methods, with an important role for video observation of real life problem-solving teams. To study the problem-solving process I used the fine-grained six phase method of structured problem solving. This structured approach is often used in continous improvment strategies like Lean and Kaizen. To better understand the group problem-solving mechanism, theory from the Organizational Behaviour domain namely goal-setting, commitment-to-change, and sensemaking was used. My three empirical studies resulted in 1) a method to code, visualize and measure the group members’ problem-solving process and its quality, 2) process factors related to achieving measurable operational performance improvements, and 3) the impact of individual problem-solving preferences on the quality of the problem-solving process. With this research I contribute to theory more insights and a better understanding of the phase-based process of group problem solving. As for the practical relevance, the results of this research hold direct relevance for problem-solving groups and their facilitators, enhancing their ability to achieve operational performance improvements

Experimental Realization of a One-way Quantum Computer Algorithm Solving Simon's Problem

We report an experimental demonstration of a one-way implementation of a quantum algorithm solving Simon's Problem - a black box period-finding problem which has an exponential gap between the classical and quantum runtime. Using an all-optical setup and modifying the bases of single-qubit measurements on a five-qubit cluster state, key representative functions of the logical two-qubit version's black box can be queried and solved. To the best of our knowledge, this work represents the first experimental realization of the quantum algorithm solving Simon's Problem. The experimental results are in excellent agreement with the theoretical model, demonstrating the successful performance of the algorithm. With a view to scaling up to larger numbers of qubits, we analyze the resource requirements for an n-qubit version. This work helps highlight how one-way quantum computing provides a practical route to experimentally investigating the quantum-classical gap in the query complexity model.Comment: 9 pages, 5 figure

Parameter Selection and Pre-Conditioning for a Graph Form Solver

In a recent paper, Parikh and Boyd describe a method for solving a convex optimization problem, where each iteration involves evaluating a proximal operator and projection onto a subspace. In this paper we address the critical practical issues of how to select the proximal parameter in each iteration, and how to scale the original problem variables, so as the achieve reliable practical performance. The resulting method has been implemented as an open-source software package called POGS (Proximal Graph Solver), that targets multi-core and GPU-based systems, and has been tested on a wide variety of practical problems. Numerical results show that POGS can solve very large problems (with, say, more than a billion coefficients in the data), to modest accuracy in a few tens of seconds. As just one example, a radiation treatment planning problem with around 100 million coefficients in the data can be solved in a few seconds, as compared to around one hour with an interior-point method.Comment: 28 pages, 1 figure, 1 open source implementatio

Improved Linear Precoding over Block Diagonalization in Multi-cell Cooperative Networks

In downlink multiuser multiple-input multiple-output (MIMO) systems, block diagonalization (BD) is a practical linear precoding scheme which achieves the same degrees of freedom (DoF) as the optimal linear/nonlinear precoding schemes. However, its sum-rate performance is rather poor in the practical SNR regime due to the transmit power boost problem. In this paper, we propose an improved linear precoding scheme over BD with a so-called "effective-SNR-enhancement" technique. The transmit covariance matrices are obtained by firstly solving a power minimization problem subject to the minimum rate constraint achieved by BD, and then properly scaling the solution to satisfy the power constraints. It is proved that such approach equivalently enhances the system SNR, and hence compensates the transmit power boost problem associated with BD. The power minimization problem is in general non-convex. We therefore propose an efficient algorithm that solves the problem heuristically. Simulation results show significant sum rate gains over the optimal BD and the existing minimum mean square error (MMSE) based precoding schemes.Comment: 21 pages, 4 figure