Intelligent Agents

1. Intelligent Agents

2. Overview • Agents • Rational Agents • Measuring rationality • Task Environments • PEAS Analysis • Properties • Agent Structure • Types of Agents COSC 159 - Fundamentals of AI

3. Agents • An agent is something that acts • In this class, we will build software agents • Agents that act rationally • How are agents different from other programs? • Autonomous • Perceptive • Persistent • Adaptable • Assume the goals of other agents COSC 159 - Fundamentals of AI

4. Agents Agent Percepts Sensors ? Environment Actions Actuators COSC 159 - Fundamentals of AI

5. Definitions • Percept sequence • History of everything agent has perceive • Agent function • Map from percept sequence to action • Agent program • Implementation of agent function COSC 159 - Fundamentals of AI

6. Example • Consider a world that has a starving monkey and a banana. Whenever the monkey is in the same location as the banana, the monkey will eat it. After eating the banana, the monkey falls asleep. • We would like to build a simulation for the environment with a software agent representing the monkey. • Consider a world with two locations. COSC 159 - Fundamentals of AI

7. Example U D COSC 159 - Fundamentals of AI

8. Example • Assumptions • Monkey can see the bananas and knows its location • Defines percepts: (Location, Contents) • Actions • Up, down, eat, sleep COSC 159 - Fundamentals of AI

9. Example • Agent function should move monkey to the bananas, eat the bananas, then sleep • One possible agent program is to create a table mapping a percept sequence to appropriate action • Table-driven agent COSC 159 - Fundamentals of AI

10. Table COSC 159 - Fundamentals of AI

11. Questions to ponder • Is a table driven agent a good way to implement rational behavior? • Are all sequences of percepts possible in the environment? • What if the monkey didn’t know its location, could you still devise a solution to the problem? How would the percepts change? COSC 159 - Fundamentals of AI

12. Measuring Rational Behavior • What does it mean for an agent to do the right thing? • The right action is the one causing the agent to be most successful. • A performance measure embodies the criterion for an agent’s success. COSC 159 - Fundamentals of AI

13. Performance Measures • Simple performance measure for monkey and bananas • The monkey has eaten and fallen asleep. • Suppose you have two monkeys, one that sleeps right after eating and one that wanders around and then falls asleep. Which one is better? Why? COSC 159 - Fundamentals of AI

14. Performance Measures • Consider more complex environments • What performance measure is appropriate for the economy? • What about for stocks? • How about medical diagnoses? • What about driving a car? • Performance measures are not easy to determine, but you must design one for each environment COSC 159 - Fundamentals of AI

15. Rationality • Rational behavior at any given time depends on four things • Performance measure • Agent’s prior knowledge • Actions agent can perform • Agent’s percept sequence COSC 159 - Fundamentals of AI

16. Rational Agents • Definition of a rational agent (R&N, pg. 36) 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. COSC 159 - Fundamentals of AI

17. Task Environments • Before building an agent, we need to understand the assumptions under which it will execute • Task environment • Performance measure • Environment • Actuators • Sensors COSC 159 - Fundamentals of AI

18. PEAS Analysis COSC 159 - Fundamentals of AI

19. Environment Properties • Task environments are unlimited, but we can categorize them among several dimensions • Fully vs. Partially Observable • How much knowledge of the environment is available to the agent • Deterministic vs. stochastic • Next state completely determined by action vs. next state not completely determined • If environment is deterministic except for actions of other agents, it’s strategic COSC 159 - Fundamentals of AI

20. Environment Properties • Episodic vs. Sequential • Episodic is agent perceiving then performing a single action. Previous episodes don’t matter. • In sequential environments, actions could affect future. • Static vs. dynamic • Does the environment change while the agent is deciding to perform an action? COSC 159 - Fundamentals of AI

21. Environment Properties • Discrete vs. Continuous • Applies to state of an environment: time; percepts; actions • If finite number of possible states, discrete • Infinite number of possible states, generally continuous. • Single agent vs. multiagent • Number of agents in the environment simultaneously • Competitive vs. cooperative COSC 159 - Fundamentals of AI

22. Environment Properties COSC 159 - Fundamentals of AI

23. Structure of Agents • Agents consist of architecture and program • Architecture defines the physical sensors and actuators for the agent • Program implements the agent function mapping percepts to actions • We will focus on designing agent programs, simulating any physical sensors and actuators in software. COSC 159 - Fundamentals of AI

24. Basic Kinds of Agents • Simple reflex agents • Model-based agents • Goal-based agents • Utility-based agents COSC 159 - Fundamentals of AI

25. Simple Reflex Agents Agent Percepts Sensors What the world is like now Environment Condition-action rules What action I should do now Actions Actuators COSC 159 - Fundamentals of AI

26. Simple Reflex Agents • Select actions on the basis of current percept • Agent function implemented with collection of condition-action rules ifbananas-in-the-roomthencommence-eating • Condition action rules are sometimes called situation-action rules, productions, or if-then rules. COSC 159 - Fundamentals of AI

27. Simple Reflex Agents • These agents are easy to build, but … • Limited intelligence • Require environment to be fully observable • Suppose only percepts for monkey are (Contents), can you write condition-action rules that will work for all possible two location environments? COSC 159 - Fundamentals of AI

28. Model-based Reflex Agents Percepts Sensors State How the world evolves What the world is like now What my actions do Environment Condition-action rules What action I should do now Actions Actuators Agent COSC 159 - Fundamentals of AI

29. Model-based Reflex Agents • Incorporates knowledge of “how the world works” • Simulation, mathematics, logic, Bayesian networks, etc. • This is called a model • The monkey can keep track of which locations it has visited, if it uses a model of movement. COSC 159 - Fundamentals of AI

30. Goal-based Agents Percepts Sensors State How the world evolves What the world is like now What my actions do What will it be like if I do action A Environment What action I should do now Goals Actions Actuators Agent COSC 159 - Fundamentals of AI

31. Goal-based Agents • A goal describes situations that are desirable • Goal selection can be • easy: if performing a single action results in achieving goal • hard: requires several actions to be taken before goal is achieved • Searching and planning are frequently used with goal-based agents COSC 159 - Fundamentals of AI

32. Goal-based Agents • Adds flexibility over reflex agents • Can handle change of goals, whereas reflex agents need to have rules modified • Have problems when • Conflicting goals • Several possible ways to achieve a goal COSC 159 - Fundamentals of AI

33. Utility-based Agents Percepts Sensors State How the world evolves What the world is like now What my actions do What will it be like if I do action A Environment Utility How happy will I be? What action I should do now Actions Actuators Agent COSC 159 - Fundamentals of AI

34. Utility-based Agents • Rather than simple binary measure of “happy” and “unhappy”, include a measure of happiness called utility • A utility function maps a state onto a real number • Embodies tradeoffs for conflicting goals • safe vs. speedy driving • Embodies measures of several goals, none of which are guaranteed to be achieved • I’m hungry. Should I play the lottery or search for food in the desert? COSC 159 - Fundamentals of AI