A COMPUTER ORIENTED VELOCITY ANALYSIS INTERPRETATION TECHNIQUE

Geophysics ◽  
1974 ◽  
Vol 39 (5) ◽  
pp. 619-632 ◽  
Author(s):  
John E. Beitzel ◽  
James M. Davis
Keyword(s):  
Decision Making ◽  
Distance Measure ◽  
Computer Time ◽  
Analysis Data ◽  
Velocity Analysis ◽  
Graph Theoretic ◽  
Complex Decision Making ◽  
Complex Decision ◽  
Complex Constraints ◽  
Interval Velocities

Extensive velocity analysis interpretation can impose a substantial man‐hour cost. A computer‐implemented technique is described which edits the velocity analysis data, thereby reducing the interpreter’s task. The technique uses graph theory to simulate the complex decision‐making inherent in the interpreter’s method of velocity analysis interpretation. The heart of the technique lies in defining a distance measure between candidate time‐velocity points from the velocity analysis. This metric is a function of the specific and potentially complex constraints imposed by the interpreter and of the weighted separation of the points. The graph theoretic techniques employed use the metric to join and, hence, select appropriate points from the candidate points while rejecting those which are invalid. Additional editing, based in part on implied interval velocities, further reduces the bulk of data presented to the interpreter. The method makes efficient use of computer time and has yielded encouraging results, as demonstrated by examples.

scholarly journals Goal-driven active learning

2021 ◽  
Vol 35 (2) ◽  
Author(s):  
Nicolas Bougie ◽  
Ryutaro Ichise
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Learning Process ◽  
Real World ◽  
Imitation Learning ◽  
Learning Approaches ◽  
Wide Range ◽  
Fixed Set ◽  
Complex Decision Making ◽  
Complex Decision

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.

The effect of the cognitive demands of the distraction task on unconscious thought

2014 ◽  
Vol 37 (1) ◽  
pp. 44-45 ◽  
Author(s):  
Laurent Waroquier ◽  
Marlène Abadie ◽  
Olivier Klein ◽  
Axel Cleeremans
Keyword(s):  
Decision Making ◽  
Unconscious Thought ◽  
Cognitive Demands ◽  
The Unconscious ◽  
Unconscious Processes ◽  
Distraction Task ◽  
Complex Decision Making ◽  
Complex Decision

AbstractThe unconscious-thought effect occurs when distraction improves complex decision making. Recent studies suggest that this effect is more likely to occur with low- than high-demanding distraction tasks. We discuss implications of these findings for Newell & Shanks' (N&S's) claim that evidence is lacking for the intervention of unconscious processes in complex decision making.

Sign in / Sign up

Export Citation Format

Share Document