Download to read offline
Uploaded byVanessa Camilleri
ICS2208 Lecture4 Intelligent Interface Agents.pdf
This document provides an overview of intelligent user interfaces and interface agents. It discusses agent models and characteristics of agents in complex systems. Environment properties and task environments in AI that affect agent decision making are described. The document also covers intelligent agent models and examples. It discusses how interface agents learn and perceive their environment, including through machine learning algorithms. Challenges of using reinforcement learning for interface agents are also outlined.
More Related Content
Similar to ICS2208 Lecture4 Intelligent Interface Agents.pdf
ICS2208 Lecture4 Intelligent Interface Agents.pdf
- 1. Intelligent User Interfaces ICS2208 vanessa.camilleri@um.edu.mt Dr VanessaCamilleri Department of AI, University of Malta
- 2. Topic 4: Overview •Agents & Intelligent Agents • Agent Models • Bene fi ts of user adaptivity • Usability challenges • Collecting data from users • Future needs in IUI’s 2
- 3. Complex Systems: Arti fi cial &Natural • Emergence • Self-organisation • Adaptation • Networks • Dynamics
- 4. Characteristics of Agentsin Complex Systems • Heterogeneity • Local Interactions • Adaptation • Emergence • Self-organisation
- 5. Examples of Agentsin Complex Systems • Ant Colonies • Economic Markets • Immune System • Traf fi c Systems
- 6. Importance of Understanding andManaging Complex Systems • Predicting System Behaviour • Designing Arti fi cial Complex Systems • Managing Natural and Social Systems
- 7. Agent-Based Models • CapturingEmergent Phenomena • Exploring Heterogeneity and Adaptation • Simulating Non-Linearity and Feedback Loops • Providing a Natural Description of Systems • Flexibility and Scalability
- 8. Environment as aProblem Space for Agents • Agent-Environment Interaction • Control and In fl uence • Non-Determinism • Effectoric Capabilities • Preconditions for Actions
- 9. Environment Properties affecting AgentDecision-Making • Accessible vs. Inaccessible • Deterministic vs. Non-Deterministic • Episodic vs. Non-Episodic • Static vs. Dynamic • Discrete vs. Continuous
- 10. Task Environments inAI • Fully Observable vs. Partially Observable • Deterministic vs. Stochastic • Competitive vs. Collaborative • Single agent vs. Multi-agent
- 11. Task Environments inAI • Dynamic vs. Static • Discrete vs. Continuous • Episodic vs. Sequential • Known vs. Unknown
- 12. Agent Interaction • Perception •Decision Making • Action • Feedback Loops • Communication (in Multi-agent Systems) • Learning and Adaptation
- 13. Intelligent Agent Models •Reactive Agents • Belief-Desire-Intention Agents • Layered Architectures • Model-based Agents • Goal-based Agents • Utility-based Agents • Learning Agents • Hierarchical Agents
- 14. Intelligent Agent Examples •Telecommunication Systems • Personal Digital Assistants • Information Management • Information Economies • Business Applications • Air Traf fi c Control • Computer Simulation • Transportation Management • Financial Management • Healthcare • Smart Homes • Robotics • Gaming • Autonomous Vehicles • Customer Service
- 15. Interface Agents • Rolesand Components • Applications: • AI Assistants • Machine Learning • IT Service Management • Network Management • Software Development
- 16. Interface Agents Examples •Email Sorting Agents • Personal Digital Assistants • R e c o m m e n d a t i o n Systems • Calendar Management Agents • Navigation and Travel Agents • Learning Interface Agents • Character-based Agents • Microsoft Of fi ce Assistant • Web Browsing Assistant • Automated Customer Service Agent • H e a l t h a n d F i t n e s s Coaches • Smart Home Controllers
- 17. How Interface AgentsLearn about their Environment • Direct Programming • Machine Learning • Reinforcement Learning • Observation and Imitation • User Feedback • Training by Example • Communication with other Agents • User and Domain Models
- 18. How Interface Agents Perceivetheir Environment • Sensors • Data Processing • Direct Programming and Rules • Machine Learning and Adaptation • Communication with Other Agents • Active vs. Passive Sensing • Perception Policies
- 19. ML Algorithms usedby IAs to Perceive Environment • Deep Learning • Reinforcement Learning • Interactive Machine Learning • Deep Neural Networks • Terrain Traversability Analysis • Vision Deep Learning
- 20. Unsupervised ML Techniquesto Improve Perception Techniques • Clustering • Dimensionality Reduction • Anomaly Detection • Association Rule Learning • Feature Learning
- 21. Challenges when using UnsupervisedML Techniques • Critical Flaws and Prospective Possibilities in Data • Veri fi cation, Validation and Trust • Absence of Guidance and Nonsensical Results
- 22. Reinforcement Learning for InterfaceAgents • Agent-Interface Environment in Reinforcement Learning • Reinforcement Learning Agents • Learning Interface Agents • Challenges and Considerations
- 23. Reinforcement Learning Algorithms forInterface Agents • Deep Q-Network • Proximal Policy Optimisation • Deep Deterministic Policy Gradient • Soft Actor Critic • Rainbow • Monte Carlo Tree Search • Reinforce
- 24. Challenges for usingReinforcement Learning for Interface Agents • Sparse and Delayed Rewards • Exploration vs. Exploitation Dilemma • Sample Ef fi ciency • High Dimensional State Space • C r e d i t A s s i g n m e n t Problem • Generalisation Across Tasks • S a f e t y a n d E t h i c a l Concerns • Partial Observability • Scalability • Evaluation and Validation