Improving Choice Mechanisms within the BVG Architecture

The BVG agent architecture relies on the use of values (multiple dimensions against which to evaluate a situation) to perform choice among a set of candidate goals. Choice is accomplished by using a calculus to collapse the several dimensions into a function that serialises candidates. In our previous experiments, we have faced decision problems only with perfect and complete information. In this paper we propose new experiments, where the agents will have to decide in the absence of all the needed and relevant information. In the BVG model, agents adjust their scale of values by feeding back evaluative information about the consequences of their decisions. We use the exact same measures to analyse the results of the experiments, thus providing a fair trial to the agents: they are judged with the same rules they can use for decision. Our method, based on values, is a novel approach for choice and an alternative to classical utilitarian theories.