In many domains, a Bayesian network's topological structure is not known a priori and must be inferred from data. This requires a scoring function to measure how well a proposed network topology describes a set of data. Many commonly used scores such as BD, BDE, BDEU, etc., are not well suited for class discrimination. Instead, scores such as the class-conditional likelihood (CCL) should be employed. Unfortunately, CCL does not decompose and its application to large domains is not feasible. We introduce a decomposable score, approximate conditional likelihood (ACL) that is capable of identifying class discriminative structures. We show that dynamic Bayesian networks (DBNs) trained with ACL have classification efficacies competitive to those trained with CCL on a set of simulated data experiments. We also show that ACL-trained DBNs outperform BDE-trained DBNs, Gaussian naive Bayes networks and support vector machines within a neuroscience domain too large for CCL.

In this paper, we present the use of stochastic learning automata (SLA) in multiagent robotics. In order to fully utilize and implement learning control algorithms in the control of multiagent robotics, an environment for simulation has to be first created. A virtual laboratory for simulation of autonomous agents, called V-Lab is described. The V-Lab architecture can incorporate various models of the environment as well as the agent being trained. A case study to demonstrate the use of SLA is presented.

A new approach to situation assessment is an automated distributed sensor network (DSN) consisting of many "intelligent" sensor devices that can pool their knowledge to achieve an accurate overall assessment of a situation. Laboratory experiments were conducted to investigate potential DSN organizations and to ascertain some general design principles. These experiments have been performed with a network of "sensor nodes," each of whom sees only a small portion of the entire environment and attempts to identify the environmental mobile entities as quickly as possible. To do this, they must cooperatively communicate their hypotheses and data, using a limited number of messages. Two general DSN organizations were tested. The first was hierarchical. The second was an "anarchic committee" whose nodes could each send messages to one, some, or all other nodes. The performance of the committee organization consistently surpassed the hierarchical one. This lent support to the contention that DSN architectures need to emphasize cooperative aspects of problem-solving. A machine-based simulation of such a network that achieved performance levels comparable to that of the human committee DSN organization was also constructed and tested. Because most situation assessment communications concern hypothesis updating and revision, minimizing communication requirements through the concept of active "hypothesis processes," which are responsible for predicting their own evolution over time, is suggested.

The ability of a set of simple predicates to capture characteristic patterns in a parametric representation of vowels in continuous speech was investigated with the aid of an efficient conjunctive pattern recognition and classification system. The results compare favourably with those produced by a cluster-based minimal Euclidean distance technique, run over the identical training and test samples. The predicates used are similar to auditory receptive fields.