We compare three approaches to solving digital jigsaw puzzles with wrap-around connections: human-only, swarm-only, and a hybrid approach that requires humans to interact with the swarm in a high-level, scalable manner. Using an iterative improvement strategy, some positive aspects of the human solvers migrate to the swarm-only approach. As the swarm-only approach gets better, humans continue to assist and the hybrid outperforms either of the independent approaches. This strategy for improving swarms is general, and continuously applicable. We show that even after many iterations and significant improvements to the swarm-only approach, support from a human improves the performance of the swarm.

This paper presents a new technique, the hypothesis swarm problem-solving technique (HSPT), that allows swarms to operate on more complex problems in a general way that produces solutions faster than traditional swarms by emerging compatible sub-solutions cooperatively. Instead of agents simply reacting to their own stimuli, they produce hypotheses about their environment to resolve discovered conflicts. As they gather supporting or refuting evidence for their hypothesis, we force a higher level of social interaction - requiring them to compare their evidence with other agents. They reward compatible hypotheses and devalue conflicting ones, encouraging acceptance of mutually supporting hypotheses. We demonstrate the effectiveness of this approach on graph-coloring and round-robin scheduling problems and also compare its efficiency with other swarm techniques including brute force, reactive swarms and ant colony optimization algorithms.

To gain insight into swarm algorithms, researchers can study insect societies and other natural collectives, program multi-agent software simulations or build groups of cooperating robots. In our research, we consider another resource: swarms of humans. Human swarms provide three primary benefits: quick feedback and evaluation of swarm algorithms, experience with high-level swarm directives instead of low-level agent programs, and a source of swarm algorithms that can potentially be reverse-engineered for use in other applications. Planning is a human's preferred problem solving methodology because we are intelligent creatures with high-level communication skills. Due to the intelligence of the agents, human swarms can be quickly programmed, for subsequent observation and analysis. This paper describes human swarm experiments designed for gathering information on swarm algorithms. At these events 100 volunteers, wearing data-encoded T-shirts, work together to perform tasks of differing degrees of complexity. Researchers provide simple instructions for each task (programming the swarm), record the swarm's behavior (videotaped observation) and analyze the results (problem identification and algorithm-mining). We demonstrate the viability of this research by presenting the quick identification of a swarm algorithm "bug" and by producing a software implementation of a swarm algorithm gleaned from our observations.

CWRU has participated in a joint project with IBM in developing an internal regression testing tool called Test Manager. The objective of Test Manager is to aid in regression testing at the unit testing, integration testing, or function testing levels and also to produce a reduced regression test set to verify the software changes indicated by the user. A conceptual basis for the tool, called a firewall, and a description of the factors which influenced the design of the tool are given. The tool utilizes a Prolog database; a set of experiments that determined how that database should best be structured is described. Timing and space requirements and future work and enhancements to the tool are discussed.>