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.

Architecting systems designed to elicit group-level behavior beyond the capability of any single agent, however, demands a labor and experimentation-intensive cycle on the part of the programmer. As part of a system to evolve swarm behaviors, we have developed a mixed radix fitness function to overcome the problems encountered with typical fitness functions when used in a multi-objective optimization problem. In this work, we show that mixed radix fitness functions can be used to encode sequential dependencies and prioritize metrics within the context of agent-based swarm behavior. To demonstrate the effectiveness of our approach, we construct a mixed radix fitness function and evolve swarm algorithms to solve a complex extension of the classic object collection problem. Further, we show the mixed radix fitness function is successful in driving evolution towards a feasible solution while avoiding local extrema.

Current Mars exploration and science is limited to orbiters and areas close to original rover landing sites. Most of the places of geological interest lay many kilometers outside of suitable landing sites. In-situ resources such as wind can enable rovers to travel great distances on Mars while using little internal power. In this paper, a dynamic model of an individual wind driven rover is used to enhance a stochastic simulation of multiple rovers traversing the Martian environment. The results from this simulation support the claim that a group of rovers equipped with minimal control mechanisms or internal energy sources can autonomously disperse and explore Mars.

In this paper we describe emergence-oriented programming (EOP), a novel, human-centric technique to engineer swarm algorithms at a higher level of complexity than those developed with simple reactive agents. The process is iterative, building modules of behavior that can be layered to produce solutions that converge faster than reactive swarms to the desired emergent goal. The layers are modular and can be independently applied, mirroring the arbitrarily nested cognitive model proposed by Baas and Emmeche. The layers are produced by external observers recognizing and reinforcing patterns within swarms that are not visible at lower levels. Each layer builds upon the previous one leading to emergence, but the entire hierarchy can be mechanically collapsed into executable if-then rules based on robot primitives. We demonstrate portions of this technique to improve on the reactive swarm approach for solving the 4-color mapping problem

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.

A collection of agents, faced with multiple tasks to perform, must effectively map agents to tasks in order to perform the tasks quickly with limited wasted resources. We propose a decentralized control algorithm based on synchronized random number generators to enact a cooperative task auction among the agents. The algorithm finds probabilistically reasonable solutions in few rounds of bidding. Additionally, as the length of the auction increases, the expectation of a better solution increases. This algorithm is not intended to find the optimal solution; it finds a good solution with less computation and communication.

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.

Traditional control approaches fall well short of the necessary flexibility and efficiency needed to meet the commercial and military demands placed upon UAV swarms. Effective coordination of these swarms requires development of control strategies based on emergent behavior. We have developed a rule-based, decentralized control algorithm that relies on constrained randomized behavior and respects UAV restrictions on sensors, computation, and flight envelope. To demonstrate and evaluate the effectiveness of our approach, we have created a simulation of an air vehicle swarm searching for and mapping a chemical cloud within a patrolled region. We then consider several different detection and mapping strategies based on emergent behavior. We then establish an inverse linear relation between the size of the swarm and the time to detect the cloud, regardless of the size of the cloud. Further, we also show the size of the swarm has a linear relation with the successful detection of the cloud.

Efficient parallel execution of a high-level data-parallel language based on nested sequences, higher order functions and generalized iterators can be realized in the vector model using a suitable representation of nested sequences and a small set of transformational rules to distribute iterators through the constructs of the language.