Marc Kirschenbaum

Decentralized cooperative auction for multiple agent task allocation using synchronized random number generators

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.

An aspect-oriented approach for modeling self-organizing emergent structures

Multi-agent systems must be engineered to ensure that desirable system-level properties will consistently emerge from the complex interactions of the underlying agents, while also guaranteeing that undesirable behavior will be suppressed. We present an Aspect-Oriented Programming (AOP) framework for modeling, visualizing and manipulating emergent structure in multi-agent systems. By encapsulating the macroscopic structure, we can identify undesirable patterns of behavior at a higher level of abstraction. The identification of such patterns allows us to implement a feedback loop to steer the behavior of the lower level agents towards actions favorable for the emergence of a reliable solution. AOP facilitates the modeling of the system-wide behavior, thus it serves as a valuable tool for building confidence that a given multi-agent system will consistently meet its requirements.

Relating logic programs via program maps

This paper presents a mathematical theory underlying a systematic method for constructingProlog programs calledstepwise enhancement. Stepwise enhancement dictates building a program starting with askeleton program which constitutes the basic control flow for the problem to be solved, and adding extra computations to the skeleton program by using well-understood programming techniques. Each extra computation can be developed independently, and the separate enhancements combined to produce the final program. While intuition and motivation have focused onProlog, the methods are applicable to logic programming languages more generally. The central concept in our mathematical theory for stepwise enhancement is that of a program map between two logic programs. Our definition of a program map from an enhancement to its skeleton guarantees the lifting of computations, the essence of the enhancement methodology. In this paper, we give definitions of program map and extensions, show that the definitions preserve the property of computations lifting, give examples of extensions and programming techniques which generate them, and point to directions for future work.

Emergence-Oriented Programming

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

Human-swarm hybrids outperform both humans and swarms solving digital Jigsaw puzzles

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.

Perceptualization of particle swarm optimization

Through visualization humans are able to perceive the efficiency of particle swarms with respect to several levels of applied inertia as well as the inclusion or exclusion of dampening. We also are able to find relationships between these levels, the diversity of a swarm, and the swarms efficiency in finding the minimum for five typical particle swarm optimization functions. This makes it possible to look at new areas of investigation to understand the connection between individual actions and emergent behavior. This paper demonstrates how to blend human intelligence, by using both their visual systems and their deductive reasoning with a swarms computational intelligence to produce results better than each could achieve independently.

Behavioral feedback as a catalyst for emergence in multi-agent systems

Swarm algorithms rely on randomness to produce solutions for complex problems. The random selection of actions and chance interactions of agents force a swarm to attempt many behavioral possibilities - reinforcing the productive ones and dampening the dead ends. Randomness however, is a dual-edged sword: it is necessary to insure a wide range of agent behavior, but also a source of inefficiency and wasted resources. Using behavioral feedback, we reinforce effective use of randomness - using it to select from a restricted list of useful actions. By observing an agents behavior over the three domains of time, space, or category, we establish a context for the application of randomness. The set of possible agent actions can be reduced to only those that are potentially beneficial. With this constraint, our results show we can dramatically improve performance and induce faster emergence from swarm algorithms using behavioral feedback

Exploratory scaling of human-swarm hybrid systems to manage simultaneous scenarios

Humans and swarms have different problem-solving skillsets. Hybrid systems blending those skillsets in an effective manner can produce a solution that outperforms either separately. To be effective, this blending must be done in a scalable way. This investigation demonstrates that by limiting the humans interaction with the swarm to broad, high-level commands, the humans contribution can be scaled to multiple, simultaneous scenarios. The improved performance of the hybrid system of one human and one swarm solving one digital jigsaw puzzle, does not degrade when the same human forms a hybrid systems with up to four separate swarms at the same time.

Experiencing multiple levels of emergence: a game designed to illustrate both the individual and collective perspective

We propose a new card game to allow users to experience two levels of an emergent process in the same context. Our goal is to help swarm programmers to better understand the relationship between actions at the agent level and the resulting behavior at the swarm level.