A Hybrid Architecture for Learning Reactive Sequential Decision Making

A Hybrid Agent Architecture for Reactive Sequential Decision Making

An RBF Network Alternative for a Hybrid Architecture

| Although our previous model CLARION has shown some measure of success in reactive sequential decision making tasks by utilizing a hybrid architecture which uses both procedural and declarative learning, it suuers from a number of problems because of its use of back propagation networks. CLARION-RBF is a more parsimonious architecture that remedies some of the problems exhibited in CLARION by ...

Matrix Sequential Hybrid Credit Scorecard Based on Logistic Regression and Clustering

The Basel II Accord pointed out benefits of credit risk management through internal models to estimate Probability of Default (PD). Banks use default predictions to estimate the loan applicants’ PD. However, in practice, PD is not useful and banks applied credit scorecards for their decision making process. Also the competitive pressures in lending industry forced banks to use profit scorecards...

Towards a Generic Architecture for Multi-Vehicle Autonomy

We describe the motivation for a programme of research “MP012: Software Architecture for Hybrid Decision Making” that is being undertaken in SEAS DTC Year 2. There are many ways to endow an autonomous system with intelligent behaviour, including planning, machine learning, and rule-based methods. Each of these has advantages in certain settings (scenarios, mission phases, etc) that we will aim ...

Bottom-up Skill Learning in Reactive Sequential Decision Tasks

This paper introduces a hybrid model that unifies connectionist, symbolic, and reinforcement learning into an integrated architecture for bottom-up skill learning in reactive sequential decision tasks. The model is designed for an agent to learn continuously from on-going experience in the world, without the use of preconceived concepts and knowledge. Both procedural skills and high-level knowl...