Intelligent agents part ii
- 1. Introduction to Artificial Intelligence Dr. Mazhar Ali Dootio Faculty of Computing and InformationTechnology Artificial Agents Part-II Reference Book: Artificial Intelligence: A Modern Approach
- 2. Types of agent programs Types of IntelligentAgents Simple reflex agents Model-based reflex agents Goal-based agents Utility-based agents LearningAgents 2 Agent Categories • Robotic Agent • Software Agent
- 3. Simple reflex agents It uses just condition-action rules The rules are like the form “if … then …” efficient but have narrow range of applicability Because knowledge sometimes cannot be stated explicitly Work only if the environment is fully observable 3
- 4. Model-based Reflex Agents For the world that is observable the agent has to keep track of an internal state That depends on the percept history Reflecting some of the unobserved aspects E.g., driving a car and changing lane 4
- 5. Example Table Agent With Internal State Saw an object ahead, and turned right, and it’s now clear ahead Go straight Saw an object Ahead, turned right, and object ahead again Halt See no objects ahead Go straight See an object ahead Turn randomly IF THEN 5
- 6. Goal-based agents Current state of the environment is always not enough The goal is another issue to achieve Judgment of rationality / correctness Actions chosen goals, based on the current state the current percept 6
- 7. Goal-based agents Goal-based agents are less efficient but more flexible Agent Different goals different tasks Search and planning two other sub-fields in AI to find out the action sequences to achieve its goal 7
- 8. Utility-based agents Goals alone are not enough to generate high-quality behavior E.g. meals in Canteen, good or not ? Many action sequences the goals some are better and some worse If goal means success, then utility means the degree of success (how successful it is) 8
- 9. Utility-based agents Utility has several advantages: When there are conflicting goals, Only some of the goals but not all can be achieved utility describes the appropriate trade-off When there are several goals None of them are achieved certainly utility provides a way for the decision-making 9
- 10. Learning Agents After an agent is programmed, can it work immediately? No, it still need teaching In AI, Once an agent is done We teach it by giving it a set of examples Test it by using another set of examples We then say the agent learns A learning agent 10
- 11. Learning Agents Four conceptual components Learning element Making improvement Performance element Selecting external actions Critic Tells the Learning element how well the agent is doing with respect to fixed performance standard. (Feedback from user or examples, good or not?) Problem generator Suggest actions that will lead to new and informative experiences. 11
- 13. Assignment • Develop PEAS (Performance, Environment, Actuators, Sensors) description for the following task environment: • Robot soccer player • Shopping ofAI books on the Internet • Analyze the properties of the above environments