Daniel Nikovski

Constructing Bayesian networks for medical diagnosis from incomplete and partially correct statistics

The paper discusses several knowledge engineering techniques for the construction of Bayesian networks for medical diagnostics when the available numerical probabilistic information is incomplete or partially correct. This situation occurs often when epidemiological studies publish only indirect statistics and when significant unmodeled conditional dependence exists in the problem domain. While nothing can replace precise and complete probabilistic information, still a useful diagnostic system can be built with imperfect data by introducing domain-dependent constraints. We propose a solution to the problem of determining the combined influences of several diseases on a single test result from specificity and sensitivity data for individual diseases. We also demonstrate two techniques for dealing with unmodeled conditional dependencies in a diagnostic network. These techniques are discussed in the context of an effort to design a portable device for cardiac diagnosis and monitoring from multimodal signals.

Speech recognition based on Kohonen self-organizing feature maps and hybrid connectionist systems

Describes a series of experiments on using Kohonen self-organizing maps and hybrid systems for continuous speech recognition. Experiments with different nonlinear transformations on the signal before using a neural network has been done and results compared. The hybrid system developed by the authors combines self-organizing feature maps with dynamic time warping. The experiments suggest that the combination has better performance than either of the two methods applied individually.<<ETX>>

Prognostic Expert Systems on a Hybrid Connectionist Environment

Abstract The paper describes a prognostic model for time-dependant events, developed in a hybrid connectionist and rule-based environment. The model has been employed in a practical expert system for agricultural insect prediction. Results from experiments are presented and the main advantages of using hybrid connectionist systems for prognostic expert systems are discussed: prognosis with inexact or partial data; a good correlation between the precision of the input data and the precision of the prediction; quantitative evaluation of the certainty in the final prediction; explanation features. A non-traditional time encoding scheme is proposed, with advantages relevant to time-event prognosis in hybrid connectionist systems.

Learning discrete Bayesian models for autonomous agent navigation

Partially observable Markov decision processes (POMDPs) are a convenient representation for reasoning and planning in mobile robot applications. We investigate two algorithms for learning POMDPs from series of observation/action pairs by comparing their performance in fourteen synthetic worlds in conjunction with four planning algorithms. Experimental results suggest that the traditional Baum-Welch algorithm learns better the structure of worlds specifically designed to impede the agent, while a best-first model merging algorithm originally due to Stolcke and Omohundro (1993) performs better in more benign worlds, including such model of typical real-world robot fetching tasks.

Learning probabilistic models for state tracking of mobile robots

We propose a learning algorithm for acquiring a stochastic model of the behavior of a mobile robot, which allows the robot to localize itself along the outer boundary of its environment while traversing it. Compared to previously suggested solutions based on learning self-organizing neural nets, our approach achieves much higher spatial resolution which is limited only by the control time-step of the robot. We demonstrate the successful work of the algorithm on a small robot with only three infrared range sensors and a digital compass, and suggest how this algorithm can be extended to learn probabilistic models for full decision-theoretic reasoning and planning.

Learning probabilistic models for optimal visual servo control of dynamic manipulation

We present an experiment in sequential visual servo control of a dynamic manipulation task with unknown equations of motion and feedback from an uncalibrated camera. Our algorithm constructs a model of a Markov decision process (MDP) by means of grounding states in observed trajectories, and uses the model to find a control policy based on visual input, which maximizes a prespecified optimal control criterion balancing performance and control effort.