Fostering & Adoption Service: Staff Engagement Briefing for Cumbria
This briefing outlines the council's objectives, proposed changes, and aims to improve service delivery in fostering and adoption. It includes feedback received, key changes, and the timeline for implementation. The focus is on enhancing support for children and carers in Cumbria.
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Presentation Transcript
Task Analysis and Decision Trees Chapter 8: Robotics and AI
Task Analysis Our task is to pick up toys with a robot arm, determining the proper wrist orientation. - Toys may be randomly placed at any angle. - The robot must observe and orient its hand appropriately to grasp the toy.
Decision Trees: Concept Decision trees model sequential decisions to achieve a goal. - Example: Choosing breakfast or navigating a sidewalk. - Each decision leads to new options, creating a tree-like structure.
Challenges in Decision Trees Decision trees grow exponentially with choices. - Example: Chess moves grow from 20 to millions in a few turns. - Key to manage: Prune branches to reduce decisions.
Techniques for Decision Trees 1. Forward Chaining: Start at the beginning and explore outward. 2. Backward Chaining: Start from the goal and work backward. 3. Combined Approach: Use both techniques to meet in the middle.
Factors in Decision Tree Design Considerations for decision tree design: - Data availability. - Known vs. unknown information. - Scoring or grading paths.
Optimal vs. Practical Solutions Optimal solutions require exhaustive searches but may be computationally expensive. - Practical solutions meet goals effectively within constraints. - 'Good enough' solutions often save time and resources.
