A neural network model for decision making With application in construction management

In this paper, an innovative approach is presented to decision making using self-organizing multi-layered neural networks. The model helps make a decision whether to use a conventional stick-built method or to use some degree of modularization when building an industrial process plant - a problem considered very important in construction management because of its economic impact. The objective of this paper is to show that both expert system and neural network approaches can be useful for decision making problems. However, in some situations a neural network approach can outperform the expert system approach. A brief overview of prior approach to modular construction decision making is provided in this paper and the reasons for using a neural network approach are also discussed. The architecture, knowledge representation, and training procedure for the neural network paradigms used are described. The performance of the trained neural network system and its comparison with the recommendations provided by human experts and the expert system are also presented

Neural Task Programming: Learning to Generalize Across Hierarchical Tasks

In this work, we propose a novel robot learning framework called Neural Task Programming (NTP), which bridges the idea of few-shot learning from demonstration and neural program induction. NTP takes as input a task specification (e.g., video demonstration of a task) and recursively decomposes it into finer sub-task specifications. These specifications are fed to a hierarchical neural program, where bottom-level programs are callable subroutines that interact with the environment. We validate our method in three robot manipulation tasks. NTP achieves strong generalization across sequential tasks that exhibit hierarchal and compositional structures. The experimental results show that NTP learns to generalize well to- wards unseen tasks with increasing lengths, variable topologies, and changing objectives.Comment: ICRA 201

Using fuzzy logic to integrate neural networks and knowledge-based systems

Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems