Agents in a Wild World

Models constrain the range of possible behaviors de£ned for a domain. When parts of a model are uncertain, the possible behaviors may be a data cloud: i.e. an overwhelming range of possibilities that bewilder an analyst. Faced with large data clouds, it is hard to demonstrate that any particular decision leads to a particular outcome.Even if we can't make de£nite decisions from such models, it is possible to £nd decisions that reduce the variance of values within a data cloud. Also, it is possible to change the range of these future behaviors such that the cloud condenses to some improved mode.Our approach uses two tools. Firstly, a model simulator is constructed that knows the range of possible values for uncertain parameters. Secondly, the TAR2 treatment learner uses the output from the simulator to incrementally learn better constraints. In our incremental treatment learning cycle, users review newly discovered treatments before they are added to a growing pool of constraints used by the model simulator.

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“…Other publications on treatment learning have assumed a "oneshot" use of TAR2 [24,[34][35][36]. This paper assumes an iterative approach.…”

Section: Prior Tar2 Resultsmentioning

confidence: 99%

“…This papers experiments with minimal condensation using the TAR2 treatment learner [19,24,[33][34][35][36]. A treatment learner seeks the least number of attribute ranges that most differentiate between desired and undesired behavior.…”

Section: Introductionmentioning

confidence: 99%

Condensing Uncertainty via Incremental Treatment Learning

Chiang

Feather

et al. 2003

Software Engineering With Computational Intelligence

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Agents in a Wild World

Cited by 1 publication

References 19 publications

Condensing Uncertainty via Incremental Treatment Learning

Condensing Uncertainty via Incremental Treatment Learning

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