Part I Agents in the World What are Agents and How Can They be Built?

1. 1 Artificial Intelligence and Agents

2. 2 Agent Architectures and Hierarchical Control

3. 1. 1 Artificial Intelligence and Agents
4. 1. 2 Agent Architectures and Hierarchical Control

Part II Reasoning and Planning with Certainty How can an agent represent its knowledge, reason, and plan, under the assumption that it knows both what is true in the world and the effects of its actions?

1. 3 Searching for Solutions

2. 4 Reasoning with Constraints

3. 5 Propositions and Inference

4. 6 Deterministic Planning

5. 1. 3 Searching for Solutions
6. 1. 4 Reasoning with Constraints
7. 1. 5 Propositions and Inference
8. 1. 6 Deterministic Planning

Part III Learning and Reasoning with Uncertainty How can an agent learn and reason, relaxing the assumption that it knows what is in the world?

1. 7 Supervised Machine Learning

2. 8 Neural Networks and Deep Learning

3. 9 Reasoning with Uncertainty

4. 10 Learning with Uncertainty

5. 11 Causality

6. 1. 7 Supervised Machine Learning
7. 1. 8 Neural Networks and Deep Learning
8. 1. 9 Reasoning with Uncertainty
9. 1. 10 Learning with Uncertainty
10. 1. 11 Causality

Part IV Planning and Acting with Uncertainty How can an agent plan and act, relaxing the assumption that it knows the effects of its actions and the current state of the world?

1. 12 Planning with Uncertainty

2. 13 Reinforcement Learning

3. 14 Multiagent Systems

4. 1. 12 Planning with Uncertainty
5. 1. 13 Reinforcement Learning
6. 1. 14 Multiagent Systems

Part V Representing Individuals and Relations How can an agent reason, learn, and act in more complex environments where there are individuals (things, entities, objects) with complex relations among them?

1. 15 Individuals and Relations

2. 16 Knowledge Graphs and Ontologies

3. 17 Relational Learning and Probabilistic Reasoning

4. 1. 15 Individuals and Relations