Epistemology as General Systems Theory: An Approach to the Design of Complex Decision-Making Experiments

1973 ◽  
Vol 3 (1) ◽  
pp. 117-134 ◽  
Author(s):  
I. I. Mitroff ◽  
F. Sagasti
1974 ◽  
Vol 34 (3) ◽  
pp. 831-834
Author(s):  
Robert L. Woolfolk

Concepts of General Systems Theory were invoked to highlight the decision-making character of psychotherapy and to propose a framework for increased systematization of therapeutic procedures. The formal characteristics of Lazarus' multimodal system were examined in terms of criteria specifying the conditions necessary for accountability in psychotherapy.


2021 ◽  
Vol 35 (2) ◽  
Author(s):  
Nicolas Bougie ◽  
Ryutaro Ichise

AbstractDeep reinforcement learning methods have achieved significant successes in complex decision-making problems. In fact, they traditionally rely on well-designed extrinsic rewards, which limits their applicability to many real-world tasks where rewards are naturally sparse. While cloning behaviors provided by an expert is a promising approach to the exploration problem, learning from a fixed set of demonstrations may be impracticable due to lack of state coverage or distribution mismatch—when the learner’s goal deviates from the demonstrated behaviors. Besides, we are interested in learning how to reach a wide range of goals from the same set of demonstrations. In this work we propose a novel goal-conditioned method that leverages very small sets of goal-driven demonstrations to massively accelerate the learning process. Crucially, we introduce the concept of active goal-driven demonstrations to query the demonstrator only in hard-to-learn and uncertain regions of the state space. We further present a strategy for prioritizing sampling of goals where the disagreement between the expert and the policy is maximized. We evaluate our method on a variety of benchmark environments from the Mujoco domain. Experimental results show that our method outperforms prior imitation learning approaches in most of the tasks in terms of exploration efficiency and average scores.


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