LNAI 2903 On How to Learn from a Stochastic Teacher or a Stochastic Compulsive Liar of Unknown Identity 1st Edition by John Oommen, Govindachari Raghunath, Benjamin Kuipers ISBN 9783540206460 354020646X

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ISBN 10: 354020646X
ISBN 13: 9783540206460
Author: John Oommen, Govindachari Raghunath, Benjamin Kuipers

We consider the problem of a learning mechanism (robot, or algorithm) that learns a parameter while interacting with either a stochastic teacher or a stochastic compulsive liar. The problem is modeled as follows: the learning mechanism is trying to locate an unknown point on a real interval by interacting with a stochastic environment through a series of guesses. For each guess the environment (teacher) essentially informs the mechanism, possibly erroneously, which way it should move to reach the point. Thus, there is a non-zero probability that the feedback from the environment is erroneous. When the probability of correct response is p>0.5, the environment is said to be Informative, and we have the case of learning from a stochastic teacher. When this probability is p<0.5 the environment is deemed Deceptive, and is called a stochastic compulsive liar.

This paper describes a novel learning strategy by which the unknown parameter can be learned in both environments. To the best of our knowledge, our results are the first reported results which are applicable to the latter scenario. Another main contribution of this paper is that the proposed scheme is shown to operate equally well even when the learning mechanism is unaware whether the environment is Informative or Deceptive. The learning strategy proposed herein, called CPL–ATS, partitions the search interval into three equi-sized sub-intervals, evaluates the location of the unknown point with respect to these sub-intervals using fast-converging ε-optimal L _RI learning automata, and prunes the search space in each iteration by eliminating at least one partition. The CPL-ATS algorithm is shown to be provably converging to the unknown point to an arbitrary degree of accuracy with probability as close to unity as desired. Comprehensive experimental results confirm the fast and accurate convergence of the search for a wide range of values for the environment’s feedback accuracy parameter p. The above algorithm can be used to learn parameters for non-linear optimization techniques.

LNAI 2903 On How to Learn from a Stochastic Teacher or a Stochastic Compulsive Liar of Unknown Identity 1st Edition Table of contents:

Chapter 1: Introduction to Learning from Uncertain Teachers

• 1.1. The Traditional Model of Supervised Learning
• 1.2. Learning from a Stochastic Teacher: An Overview
• 1.3. Defining the Stochastic Teacher and Stochastic Compulsive Liar Models
• 1.4. Implications of Unknown Teacher Identity and Behavior
• 1.5. Objectives of the Book and Challenges in the Field

Chapter 2: Foundations of Learning Theory

• 2.1. Classical Learning Models: A Quick Overview
• 2.2. Probabilistic Models in Learning
• 2.3. Bayesian Learning and Inference
• 2.4. Statistical Learning Theory and Generalization
• 2.5. Exploring Stochastic Behavior in Learning Contexts

Chapter 3: The Stochastic Teacher Problem

• 3.1. Characterizing the Stochastic Teacher
• 3.2. Types of Stochasticity in Learning Environments
• 3.3. The Teacher’s Identity: Known vs. Unknown
• 3.4. Learning from a Noisy Teacher: Noise Models and Assumptions
• 3.5. Strategies for Coping with Uncertainty in Learning

Chapter 4: The Stochastic Compulsive Liar Problem

• 4.1. Defining the Stochastic Compulsive Liar
• 4.2. Understanding Liar Models and Their Impact on Learning
• 4.3. Adversarial Learning: The Role of Misinformation
• 4.4. Identifying and Handling Deceptive Teachers in Learning Systems
• 4.5. Case Studies: Learning from Lies in Dynamic Environments

Chapter 5: Learning Algorithms for Stochastic Teachers and Liars

• 5.1. Classical Learning Algorithms and Their Limitations in Stochastic Settings
• 5.2. Robust Learning Algorithms: Coping with Stochastic Teachers
• 5.3. Adversarial Learning: Strategies for Counteracting Lies
• 5.4. Bayesian Inference for Handling Uncertainty
• 5.5. Online Learning and Adaptive Algorithms
• 5.6. The Role of Feedback in Learning from Uncertain Sources

Chapter 6: The Identity Problem: Unknown Teacher or Liar

• 6.1. The Problem of Unknown Identity in Learning
• 6.2. Identifying the Source: Teacher or Liar?
• 6.3. Techniques for Inferring the Identity of the Teacher
• 6.4. Decision Making with Partial Information
• 6.5. The Role of Identity Inference in Robust Learning

Chapter 7: Theoretical Foundations of Learning from Stochastic Sources

• 7.1. Mathematical Formulation of Stochastic Teacher Models
• 7.2. Probabilistic Reasoning and Stochastic Models in Learning
• 7.3. The Bias-Variance Tradeoff in Adversarial and Stochastic Settings
• 7.4. Theorems and Results in Learning with Stochastic Teachers and Liars
• 7.5. Complexity Considerations and Learning Efficiency

Chapter 8: Empirical Approaches and Experiments

• 8.1. Designing Experiments to Simulate Stochastic Teachers and Liars
• 8.2. Simulation Environments and Datasets
• 8.3. Experimental Design: Evaluation Metrics for Adversarial Learning
• 8.4. Case Study 1: Learning from Noisy Teachers in Real-World Applications
• 8.5. Case Study 2: Learning from Misinformation in Autonomous Systems
• 8.6. Results and Insights from Empirical Studies

Chapter 9: Applications of Learning from Stochastic Teachers and Liars

• 9.1. Adversarial Machine Learning: Identifying and Mitigating Misinformation
• 9.2. Autonomous Systems: Learning in the Presence of Uncertain Guides
• 9.3. Robotics: Coping with Noisy or Deceptive Training Data
• 9.4. Education and Human-Machine Interaction: Learning from Inconsistent Teachers
• 9.5. Social Media and Content Filtering: Dealing with Misinformation and Fake News

Chapter 10: Advanced Topics and Open Problems

• 10.1. Learning in Highly Dynamic Environments with Stochastic Teachers
• 10.2. Meta-Learning for Stochastic and Adversarial Scenarios
• 10.3. Learning from Multiple Stochastic Teachers and Liars
• 10.4. Ethical Considerations in Learning from Uncertain or Deceptive Sources
• 10.5. Future Directions and Open Problems in Stochastic and Adversarial Learning

Chapter 11: Conclusion

• 11.1. Summary of Key Insights
• 11.2. The Role of Robust Learning in AI and Human-Machine Collaboration
• 11.3. Final Thoughts on Learning from Stochastic and Adversarial Teachers

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