This paper reports on the Furniture Design Critic. We propose a computational model of design critiquing using the program, which as a research tool helps us explain how to select critiquing methods in the consideration of critiquing conditions. Surveying the literature of architectural education, we have identified two dimensions from critiquing comments: (1) delivery types (interpretation, in...

-Function approximation has been used extensively with rein forcement learning, even though theoretical support was based mainly on tabular representations. This paper proposes an actor-critic structure following the existing convergence proofs as much as possible. The actor and critic modules employ an adaptive neuro-fuzzy architecture based on fuzzy ARTMAP concepts and gradient descent. Resul...

This paper reviews efforts to design an environment for authoring group performance support system (GPSS) agents that can interact with remote internet resources, applications, and users. We review the architecture of the GPSS environment and show how its tutor/critic elements do and don’t map into an idealized specification extracted from the agent oriented programming and inter-agent communic...

Off-policy stochastic actor-critic methods rely on approximating the stochastic policy gradient in order to derive an optimal policy. One may also derive the optimal policy by approximating the action-value gradient. The use of action-value gradients is desirable as policy improvement occurs along the direction of steepest ascent. This has been studied extensively within the context of natural ...

We study a model of evolving populations of self-learning agents and analyze the interaction between learning and evolution. We consider agent-brokers that predict stock price changes and use these predictions for selecting actions. Each agent is equipped with a neural network adaptive critic design for behavioral adaptation. We discuss three cases in which either learning, or evolution, or bot...

This section concerns neural networks which are hybrid either in terms of structure or in terms of training algorithms. The counterpropagation network is one that incorporates structural characteristics of the Kohonen and Grossberg networks and it is trained by composite supervised–unsupervised methods. The adaptive critic concept concerns neural network implementations of reinforcement learnin...

We present an empirical investigation of a recent class of Generative Adversarial Networks (GANs) using Integral Probability Metrics (IPM) and their performance for semi-supervised learning. IPM-based GANs like Wasserstein GAN, Fisher GAN and Sobolev GAN have desirable properties in terms of theoretical understanding, training stability, and a meaningful loss. In this work we investigate how th...