'Action selection' presentation slideshows

Reinforcement Learning

2.05k views • 136 slides

956 views • 84 slides

Navigation

667 views • 44 slides

Outline

Outline. Intelligent Agents (IA) Environment types IA Behavior IA Structure IA Types. What is an (Intelligent) Agent?. An over-used, over-loaded, and misused term.

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W-Learning: Competition Among Selfish Q-Learners

W-Learning: Competition Among Selfish Q-Learners. Presented by Alp Sarda?. Autonomous Mobile Robots:. Behaviour Based AI: emphasizing intelligence as emerging from ongoing interaction with the world. Subsumption Architecture: By Brooks. Ideas Of Subsumption Arcihtecture.

453 views • 34 slides

A Confidence-Based Approach to Multi-Robot Demonstration Learning

A Confidence-Based Approach to Multi-Robot Demonstration Learning. Sonia Chernova Manuela Veloso. Carnegie Mellon University Computer Science Department. Policy Development. Traditional Policy Development: Access/select sensor data Develop actions

444 views • 32 slides

A Decision-Theoretic Model of Assistance - Evaluation, Extensions and Open Problems

A Decision-Theoretic Model of Assistance - Evaluation, Extensions and Open Problems. Sriraam Natarajan, Kshitij Judah, Prasad Tadepalli and Alan Fern School of EECS, Oregon State University. Outline. Introduction Decision-Theoretic Model Experiment with folder predictor

457 views • 32 slides

Partial Order Planning Based on slides by: Carmel Domshlak

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A Cognitive Theory of Everything:

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Tutorial 4: Case Study

1.22k views • 28 slides

BehaviorNet An Action Selection Mechanism

BehaviorNet An Action Selection Mechanism. Aregahegn Negatu And Conscious Software Research Group. Intelligent agents. Agents have Drives, agenda, primary motivation Goals, subgoals Agents live in an environment Agents continuously act in pursuit of their goals/agenda. Behavior.

331 views • 25 slides

Probabilistic Planning (goal-oriented)

Probabilistic Planning (goal-oriented). Left Outcomes are more likely. Action. Maximize Goal Achievement. I. Probabilistic Outcome. A1. A2. Time 1. A1. A1. A1. A1. A2. A2. A2. A2. Time 2. Dead End. Action. Goal State. State. 1. FF-Replan. Simple replanner

393 views • 22 slides

Exploration of Unknown Environments with Motivational Agents

Exploration of Unknown Environments with Motivational Agents. Luis Macedo 1,2 , Amilcar Cardoso 2 1 Department of Informatics and Systems Engineering, Engineering Institute, Coimbra Polytechnic Institute, 3030 Coimbra, Portugal

316 views • 21 slides

Bayesian Sparse Sampling for On-line Reward Optimization

Bayesian Sparse Sampling for On-line Reward Optimization. Presented in the Value of Information Seminar at NIPS 2005. Based on previous paper from ICML 2005, written by Dale Schuurmans et all. Background Perspective. Be Bayesian about reinforcement learning

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Introduction to Reinforcement Learning

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THE BASAL GANGLIA: Neuroanatomy

THE BASAL GANGLIA: Neuroanatomy. S. J. Potashner sjp9713@neuron.uchc.edu. FUNCTIONS. Facilitates the most effective motor, emotional and cognitive behavior for the prevailing circumstances, and suppresses other behaviors. (Action selection)

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Plan Submitted By : Rahul Jaitly

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INTRODUCTION / RATIONALE

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