1. Introduction
1.1. Defining Knowledge Representation
1.2. Overview of Paper
2. Comprehensive Knowledge Representations
2.1. High-level Representations of Tasks
2.2. Cognitive Architectures
2.3. Distributive and Collaborative Representations
3. Specific Knowledge Representations
3.1. Probabilistic Graphical Models
3.2. Semantic Graphs
3.3. Context-Free Grammars
4. Model-Level Representations
4.1. Classical Machine Learning Techniques
4.2. Neural Networks for Deep Learning
4.3. Summary
5. Selecting Knowledge Representations
5.1. Component #1: Representing Motions for Action
5.2. Component #2: Perception
5.3. Component #3: Grounding Concepts with Logic
5.4. Component #4: Probabilities & Beliefs
5.5. Component #5: Learning from Experiences
5.6. Component #6: Descriptive Problem Statement
5.7. Final Analysis
6. Concluding Remarks