Reward-Predictive Clustering

Recent advances in reinforcement-learning research have demonstrated impressive results in building algorithms that can out-perform humans in complex tasks. Nevertheless, creating reinforcement-learning systems that can build abstractions of their experience to accelerate learning in new contexts still remains an active area of research. Previous work showed that reward-predictive state abstractions fulfill this goal, but have only be applied to tabular settings. Here, we provide a clustering algorithm that enables the application of such state abstractions to deep learning settings, providing compressed representations of an agent's inputs that preserve the ability to predict sequences of reward. A convergence theorem and simulations show that the resulting reward-predictive deep network maximally compresses the agent's inputs, significantly speeding up learning in high dimensional visual control tasks. Furthermore, we present different generalization experiments and analyze under which conditions a pre-trained reward-predictive representation network can be re-used without re-training to accelerate learning -- a form of systematic out-of-distribution transfer

Implementing Packaged Software

This article presents a model of the implementation process for dedicated packages and describes a research project to test the model undertaken with the cooperation of a major computer vendor. Data were collected from 78 individuals in 18 firms using the package and from the package vendor. The results of the study offer some support for the model, along with suggestions for package implementation for both the customer and package vendor.Information Systems Working Papers Serie

A STRUCTURAL MODEL OF IMPLEMENTATION

A general model of the management science implementation process is presented based on the results of more than ten years of implementation research. A multiple-equation representation of that model is developed for one important class of implementation, the two-stage implementation, in which it is necessary to gain both user and management acceptance of the system being implemented. The postulated model represents an advance in at least three ways: (a) it integrates previous findings; (b) it generalizes across settings; and (c) it is testable as a whole.Information Systems Working Papers Serie

TESTING AN INTEGRATED IMPLEMENTATION MODEL WITH DATA FROM A GENERALIZED DSS

A model proposed by Schultz, Ginzberg & Lucas (1984) that integrates the factor and process approaches to implementation was field tested with data from a generalized decision support system. Significant associations were found between manager acceptance and user perceptions of support, user personal stake and system use. The results suggest that voluntary and non-voluntary use of a system have different precursors and may be encouraged in different ways. Although the overall model receives only partial support, the results of the study suggest approaches for further testing of network models of implementation.Information Systems Working Papers Serie