1. AI in game (I) 권태경 Fall, 2006

2. outline AI definition taxonomy agents

3. What is AI? Views of AI fall into four categories: Thinking humanlyThinking rationally Acting humanlyActing rationally The textbook advocates "acting rationally"

4. Acting humanly: Turing Test Turing (1950) "Computing machinery and intelligence": "Can machines think?"  "Can machines behave intelligently?" Operational test for intelligent behavior: the Imitation Game Predicted that by 2000, a machine might have a 30% chance of fooling a lay person for 5 minutes Anticipated all major arguments against AI in following 50 years Suggested major components of AI: knowledge, reasoning, language understanding, learning

5. Thinking humanly: cognitive modeling Need to get inside the actual workings of human minds Goal –Program’s I/O and timing behaviors match corresponding human behaviors How to validate? It requires 1) Predicting and testing behavior of human subjects (top-down) or 2) Direct identification from neurological data (bottom-up) Real “cognitive science” should experiment actual humans or animals

6. Thinking rationally: "laws of thought" Aristotle: what are correct arguments/thought processes? –Syllogism Direct line through mathematics and philosophy to modern AI Problems: 1.Informal knowledge, e.g. grey zone, certainty 2.Solving a problem in principle vs. doing so in practice

7. Acting rationally: rational agent A rational agent is one that acts so as to achieve the best outcome or when there is uncertainty, the best expected outcome –E.g. autonomous control, adapt to change Correct inference cannot do the job sometimes –Acting rationally does not necessarily involve inference –E.g. a reflex action We need learning –Understanding how the world works helps to generate more effective strategies to deal with it Focus on general principles of rational agents and on components for constructing them

8. Rational agents An agent is an entity that perceives and acts Abstractly, an agent is a function from percept histories to actions: [f: P*  A ] For any given class of environments and tasks, we seek the agent (or class of agents) with the best performance Caveat: computational limitations make perfect rationality unachievable  design best program for given machine resources Percept: the agent’s perceptual inputs at any given instant

9. outline AI taxonomy agent

10. Agents An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators Human agent: –eyes, ears, and other organs for sensors –hands, legs, mouth, and other body parts for actuators Robotic agent: –cameras and infrared range finders for sensors –various motors for actuators

11. Agents and environments The agent function: an abstract mathematical description –The agent function maps from percept histories to actions: [f: P*  A ] The agent program: a concrete implementation –The agent program runs on the physical architecture to implement f

12. Vacuum-cleaner world Percepts: location and contents, e.g., [A,Dirty] Actions: Left, Right, Suck, NoOp

13. Rational agents: definition Rational Agent: For each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and whatever built-in knowledge the agent has. Performance measure: An objective criterion for success of an agent's behavior –E.g., performance measure of a vacuum-cleaner agent could be amount of dirt cleaned up, amount of time taken, amount of electricity consumed, amount of noise generated, etc.

14. Rational agents: issues Rationality is distinct from omniscience (all- knowing with infinite knowledge) Agents can perform actions in order to obtain useful information or to modify future percepts (information gathering, exploration) –Not for performance maximization An agent is autonomous if its behavior is determined by its own experience (with ability to learn and adapt) –Become independent of the prior knowledge from its design

15. PEAS: formalization PEAS: Performance measure, Environment, Actuators, Sensors Must first specify the setting for intelligent agent design Consider, e.g., the task of designing an automated taxi driver: –Performance measure –Environment –Actuators –Sensors

16. PEAS automated taxi driver: –Performance measure: Safe, fast, legal, comfortable trip, maximize profits –Environment: Roads, other traffic, pedestrians, customers –Actuators: Steering wheel, accelerator, brake, signal, horn –Sensors: Cameras, sonar, speedometer, GPS, odometer, engine sensors, keyboard or microphone

17. Environment types Fully observable (vs. partially observable): An agent's sensors give it access to the complete state of the environment at each point in time Deterministic (vs. stochastic): The next state of the environment is completely determined by the current state and the action executed by the agent –In partially observable case, it could appear to be stochastic –If the environment is deterministic except for the actions of other agents, then the environment is strategic Episodic (vs. sequential): The agent's experience is divided into atomic "episodes" –each episode consists of the agent perceiving and then performing a single action –the choice of action in each episode depends only on the episode itself

18. Environment types Static (vs. dynamic): The environment is unchanged while an agent is deliberating –The environment is semidynamic if the environment itself does not change with the passage of time but the agent's performance score does Discrete (vs. continuous): A limited number of distinct, clearly defined percepts and actions Single agent (vs. multiagent): An agent operating by itself in an environment –Entity B is an agent or merely a stochastically behaving object? Maximize its performance measure depending on agent A’s behavior –Multiagent competitive vs. cooperative Communication A hardest combination from 6 categories?

19. Environment types Chess with Chess without Taxi drivinga clock Fully observableYesYesNo DeterministicStrategicStrategicNo Episodic NoNoNo Static SemiYes No DiscreteYes YesNo Single agentNoNoNo The environment type largely determines the agent design The real world is (of course) partially observable, stochastic, sequential, dynamic, continuous, multi-agent

20. Agent functions and programs An agent is completely specified by the agent function mapping percept sequences to actions One agent function (or a small equivalence class) is rational Aim: find a way to implement the rational agent function concisely

21. Table-lookup agent Drawbacks: –Huge table –Take a long time to build the table –No autonomy –Even with learning, need a long time to learn the table entries function Table-Driven-Agent(percept) returns an action static: percepts, a sequence // initially empty table, a table of actions // indexed by percept sequences append percept to the end of percepts action <- LookUp (percepts, table) return action

22. Agent types Four basic types in order of increasing generality: –Simple reflex agents –Model-based reflex agents –Goal-based agents –Utility-based agents How to convert into learning agents

23. Simple reflex agents

24. A condition-action rule –If (condition) then (do a specific action) Interrupt-Input: abstract description of the current state Agent’ intelligence is limited –It works well when environments are fully observable –If partially observable, some problem, e.g. infinite loop, can occur –Randomized action can escape from infinite loops function Simple-Reflex-Agent(percept) returns an action static: rules, a set of condition-action rules state <- Interrupt-Input (percept) rule <- Rule-Match (state,rules) action <- Rule-Action (rule) return action

25. Model-based reflex agents

26. To handle partial observability, agent keeps track of the part of the world it cannot see now –Internal state Tries to model the world in two ways –How the world evolves independently of the agent –How the agent’s action affect the world function Model-Based-Reflex-Agent(percept) returns an action static: state, a description of the current world rules, a set of condition-action rules action, the most recent action state <- Update-State (state, action, percept) rule <- Rule-Match (state, rules) action <- Rule-Action (rule) return action

27. (model-based) Goal-based agents The agent needs goal information that describes desirable situations Consider future Search and planning to find action sequences for goal Less efficient but more flexible

28. Utility-based agents Goals alone are not enough to generate high-quality behavior sometimes –Goal are often binary distinction e.g. happy vs. unhappy A utility function maps a state (or its sequence) onto a real number, e.g. the degree of happiness –Can provide a tradeoff between conflicting goals e.g. speed vs. security If multiple goals, the likelihood of success of each goal can be weighed up against the importance of the goals

29. Utility-based agents

30. Learning agents So far, we talked about various methods for selecting actions –We have not explained how the agent programs come into being 4 major components –Learning element is responsible for making improvements Percept has no idea of how to evaluate the state of the world Uses feedback from critic –Critic tells the learning agent how well the agent is doing in terms of performance standard Note that performance standard is fixed –Performance element is responsible for selecting external actions This is the agent in the previous slides Takes percept and decides on actions –Problem generator is responsible for suggesting actions that will lead to new and informative experiences Can choose suboptimal but exploratory actions

31. Learning agents

32. reference Artificial Intelligence: A Modern Approach (Second Edition) by Stuart Russell and Peter Norvig, Prentice Hall

33. Potential project ideas Realty problem Education or Entrance exam problem