Other

Newvectors LLC

Henry Van Dyke Parunak

Sven Brueckner

John A Sauter

Peter Michael Weinstein

Robert J Bisson

Paul Chiusano

Theodore C Belding

Robert S Matthews

Steven M Brophy

A clustering method yields a searchable hierarchy to speed retrieval, and can function dynamically with a changing document population. Nodes of the hierarchy climb up and down the emerging hierarchy based on locally sensed information. Like previous ant clustering algorithms, the inventive process is dynamic, decentralized, and anytime. Unlike them, it yields a hierarchical structure. For simplicity, and reflecting our initial application in the domain of textual information, the items being clustered are documents, but the principles may be applied to any collection of data items.

Hierarchical ant clustering and foraging

A method of predicting the behavior of software agents in a simulated environment involves modeling a plurality of software agents representing entities to be analyzed, which may be human beings. Using a set of parameters that governs the behavior of the agents, the internal state of at least one of the agents is estimated by its behavior in the simulation, including its movement within the environment. This facilitates a prediction of the likely future behavior of the agent based solely upon its internal state; that is, without recourse to any intentional agent communications. In the preferred embodiment the simulated environment is based upon a digital pheromone infrastructure. The simulation integrates knowledge of threat regions, a cognitive analysis of the agent's beliefs, desires, and intentions, a model of the agent's emotional disposition and state, and the dynamics of interactions with the environment. By evolving agents in this rich environment, we can fit their internal state to their observed behavior. In realistic wargame scenarios, the system successfully detects deliberately played emotions and makes reasonable predictions about the entities' future behavior.

Characterizing and predicting agents via multi-agent evolution

A system and process uses pheromone propagation to provide direction and/or distance information to a remote destination through the use of localized decision making processes. Locally available information is exchanged and translated with global information through the use of a pheromone vocabulary and multi-layer grid systems. Self-organizing networks of sensors provide remote monitoring of a space.

Spatial coordination system

Growing community of inventors

Ann Arbor, MI, United States of America

Henry Van Dyke Parunak