1. Intelligent Agents: an Overview

2. 2 Definitions Rational behavior: to achieve a goal minimizing the cost and maximizing the satisfaction. Rational agent: Sensors to perceive the environment Effectors to change the environment A processing component that chooses how to act depending on the input, the goal, available actions, the environment.

3. 3 Terminology Percept sequence : sequence of inputs to the agent. Performance measure: a measure of our satisfaction with the agent. Utility function: used by the agent to evaluate its actions.

4. 4 Percepts and Actions In general, there is a mapping between the percept sequences and the possible actions. In general, there is a mapping between the percept sequences and the possible actions. The mapping can be represented explicitly (table look up), explicitly (table look up), as a function (e.g. square root agent), as a function (e.g. square root agent), as a set of rules as a set of rules

5. 5 Features of an Agent I The agent constructs the algorithm to accomplish the task dynamically Knowledge base Knowledge base Reasoning mechanisms Reasoning mechanisms

6. 6 Features of an Agent II The agent accumulates experience - learns from its activity and adapts Learning Learning Through interaction with the user, and /or other agents Through interaction with the user, and /or other agents It may change its knowledge base, it may change its reasoning procedures It may change its knowledge base, it may change its reasoning procedures

7. 7 The PAGE Model PAGE: Percepts Actions Goals Environment

8. 8 General Scheme of Action Analyze percepts and update memory Analyze percepts and update memory Choose best-action based on the new state of memory Choose best-action based on the new state of memory Update memory after the action Update memory after the action

9. 9 Environment Program The environment is described in terms of states Basic loop: Do until termination state: send percept to agent send percept to agent get action from agent get action from agent evaluate action and compute performance scores evaluate action and compute performance scores update environment update environment

10. 10 Types of Agents Simple reflex agents Simple reflex agents Agents that keep track of the world Agents that keep track of the world Goal-based agents Goal-based agents Utility-based agents Utility-based agents

11. 11 Reflex Agents External world IF ConditionAction THEN

12. 12 Agents that Keep Track of the World THEN External world Memory IF ConditionAction

13. 13 Goal-Based Agents Goals Condition IFTHEN Choose a rule

14. 14 Utility-Based Agents Goals Condition IFTHEN Choose a rule Utility function

15. 15 Agents and Robots Software agents Software agents Intelligent agents whose input (apart from interaction with the user) is from the virtual computer world Intelligent agents whose input (apart from interaction with the user) is from the virtual computer world Robots Robots Hardware devices Hardware devices Input - from the real world Input - from the real world Need more capabilities - motion, vision, reasoning about space. Need more capabilities - motion, vision, reasoning about space.

16. 16 Research in Robotics NASA robotics: http://robotics.jpl.nasa.gov/groups/rv/homepage.htmlhttp://robotics.jpl.nasa.gov/groups/rv/homepage.html Brown University: http://www.cs.brown.edu/research/areas/robotics.htmlhttp://www.cs.brown.edu/research/areas/robotics.html Stanford Robotics: http://ai.stanford.edu/http://ai.stanford.edu/ Humanoid Robotics at MIT AI lab: http://www.ai.mit.edu/projects/humanoid-robotics-group/ http://www.ai.mit.edu/projects/humanoid-robotics-group/ Mark Tilden's bug robots http://www.beam-online.com/Robots/Galleria_other/tilden.html Robogenetics project http://www.nis.lanl.gov/projects/robot/http://www.nis.lanl.gov/projects/robot/ The Artificial Intelligence Laboratory at the Free University of Brussels: http://arti.vub.ac.be/http://arti.vub.ac.be/