This paper generalizes and extends the theory of conceptual spaces as originally proposed by Gardenfors (Conceptual spaces. Cambridge, MA: MIT Press) to provide further geometric representations of both concepts and object observations within a multi-dimensional fuzzy space corresponding to a subset of a unit hypercube. With these representations, we are able directly to calculate normalized scalar measures both of the similarity of two different concepts and of the degree to which an observation satisfies a concept description, and thus to perform inferences with respect to situational assessments and predictions. This capability is directly relevant to the implementation of Levels 2 and 3 data fusion functions using our approach.

A concept geometry for conceptual spaces

Multiagent reinforcement learning problems are especially difficult because of their dynamism and the size of joint state space. In this paper a new benchmark problem is proposed, which involves the need for cooperation, competition and synchronization between agents. The notion of state attractor is introduced, such that agents compute their actions based on the proximity of their current state to the nearest state attractor. A genetic algorithm is used to find the state attractors. This representation can be used as a compact way to define individual or joint policies.

Evolving equilibrium policies for a multiagent reinforcement learning problem with state attractors

The complex analyses required by artificial intelligence applications need both a flexible structure for information representation and a quick and ecient method for information categorization. The latter has a great impact on the former because it can increase or not the knowledge database dimension. This situation can sometimes lead to undesired complexity. In this paper a genetic algorithm approach was used in order to obtain two major desired eects. The first one is to increase the disjunction level in knowledge representation and the second is to increase the processing speed. Comparisons were made between our solutions and the weighting results of ReliefF algorithm.

Zaharia Florin

Papers

On Finding an Optimized Categorization in Conceptual Spaces using Genetic Algorithms