Leveraging synergy for multiple agent infotaxis
- Los Alamos National Laboratory
Social computation, whether in the form of a search performed by a swarm of agents or the predictions of markets, often supplies remarkably good solutions to complex problems, which often elude the best experts. There is an intuition, built upon many anecdotal examples, that pervading principles are at play that allow individuals trying to solve a problem locally to aggregate their information to arrive at an outcome superior than any available to isolated parties. Here we show that the general structure of this problem can be cast in terms of information theory and derive general mathematical conditions for information sharing and coordination that lead to optimal multi-agent searches. Specifically we illustrate the problem in terms of the construction of local search algorithms for autonomous agents looking for the spatial location of a stochastic source. We explore the types of search problems -defined in terms of the properties of the source and the nature of measurements at each sensor -for which coordination among multiple searchers yields an advantage beyond that gained by having the same number of independent searchers. We assert that effective coordination corresponds to synergy and that ineffective coordination corresponds to redundancy as defined using information theory. We classify explicit types of sources in terms of their potential for synergy. We show that sources that emit uncorrelated particles based on a Poisson process, provide no opportunity for synergetic coordination while others, particularly sources that emit correlated signals, do allow for strong synergy between searchers. These general considerations are crucial for designing optimal algorithms for particular search problems in real world settings.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC52-06NA25396
- OSTI ID:
- 960754
- Report Number(s):
- LA-UR-08-07091; LA-UR-08-7091; TRN: US201008%%677
- Resource Relation:
- Conference: Social Computing, Behavioral Modeling, & Prediction ; March 31, 2009 ; Phoenix, AZ
- Country of Publication:
- United States
- Language:
- English
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