Introduction to Artificial Intelligence 2 nd semester 2016/2017. Chapter 2: Intelligent Agents

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1 Introduction to Artificial Intelligence 2 nd semester 2016/2017 Chapter 2: Intelligent Agents Mohamed B. Abubaker Palestine Technical College Deir El-Balah 1

2 Agents and Environments An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators A human agent has: 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 The term percept refers to the agent s perceptual inputs at any given instance. 2

3 Agents and Environments (cont..) Agent s behavior is described by the agent function that maps any given percept sequence to an action. Agent function for an artificial agent will be implemented by an agent program 3

4 Agents interact with environment through sensors and actuators 4

5 Vacuum-cleaner world 5

6 The Concept of Rationality A rational agent is one that does the right thing The right action is the one that will cause the agent to be most successful 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. 6

7 Rationality 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. The answer if a given agent is a rational agent, depends on four things: The performance measure The agent s prior knowledge of the environment The actions that the agent can perform The agent s percepts sequence to date 7

8 Omniscience, Learning, and Autonomy Rationality is distinct from omniscience (all-knowing with infinite knowledge) Rationality Perfection Rationality maximizes the expected performance Information gathering Rational agent does not require to gather information only, but also to learn as much as possible from what it perceives. Learning The agent s initial configuration could reflect some prior knowledge of the environment, but as the agent gains experience this may be modified and augmented. A rational agent should be autonomous if its behavior is determined by its own experience become effectively independent of its prior knowledge 8

9 The Nature of Environments In designing an agent, the first step must always be to specify the task environment as fully as possible Task Environment (PEAS): Performance Measure Environment Actuators Sensors 9

10 PEAS description of the task environment for an automated taxi 10

11 Properties of Task Environment Fully observable vs. Partially observable Single agent vs. multi-agent Deterministic vs. Stochastic Episodic vs. Sequential Static vs. Dynamic Discrete vs. Continuous Known vs. Unknown 11

12 Properties of Task Environment Fully observable vs. Partially observable Fully observable: An agent's sensors give it access to the complete state of the environment at each point in time. the sensors detect all aspects that are relevant to the choice of action. convenient because the agent need not maintain any internal state to keep track of the world Partially observable because of noisy and inaccurate sensors or because parts of the state are simply missing from the sensor data 12

13 Properties of Task Environment Single agent vs. multi-agent An agent solving a crossword puzzle by itself is a single agent environment Multi-agent environment Cooperative Competitive Communication Chess, taxi driving, soccer 13

14 Properties of Task Environment Deterministic vs. Stochastic Deterministic The next state of the environment is completely determined by the current state and the action executed by the agent Crossword puzzle, chess Otherwise, it is a stochastic environment. Taxi driving, dice 14

15 Properties of Task Environment Episodic vs. Sequential Episodic 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 Assembly line Sequential The current decision could affect all future decisions Chess, taxi driving 15

16 Properties of Task Environment Static vs. Dynamic Dynamic The environment can changed while an agent is deliberating (deciding on an action) Otherwise, it is a static environment The environment is semidynamic if the environment itself does not change with the passage of time but the agent's performance score does 16

17 Properties of Task Environment Discrete vs. Continuous applies to the state of the environment, to the way time is handled, and to the percepts and actions of the agent A limited number of distinct states, and discrete set of percepts and actions. Discrete Taxi driving is a continuous-state and continuous-time problem Known vs. Unknown refers not to the environment itself but to the agent s (or designer s) state of knowledge about the laws of physics of the environment 17

18 The Structure of Agents So far, describe agent by behavior: the action that is performed after any given sequence of percepts The job of AI is to design: an agent program that implements the agent function this program will run on some sort of computing device with physical sensors and actuators Agent = architecture + program The difference between the agent program and the agent function: Agent program takes the current percept as input Agent function takes the entire percept history 18

19 Table lookup Agent 19

20 Agent program for a vacuum-cleaner agent 20

21 Basic kinds of Agent Programs Simple reflex agents Model-based reflex agents Goal-based agents Utility-based agents Learning agents 21

22 Simple reflex agents The simplest kind of agent These agents select actions on the basis of the current percept, ignoring the rest of the percept history Simple reflex agents have the admirable property of being simple, but they turn out to be of limited intelligence 22

23 Simple reflex agents 23

24 Simple reflex agents 24

25 Model-based reflex agents The most effective way to handle partial observability is for the agent to keep track of the part of the world it can t see now. The agent should maintain some sort of internal state that depends on the percept history and thereby reflects at least some of the unobserved aspects of the current state 25

26 Model-based reflex agents 26

27 Model-based reflex agents 27

28 Goal-based agents Knowing something about the current state of the environment is not always enough to decide what to do the agent needs some sort of goal information that describes situations that are desirable 28

29 Goal-based agents 29

30 Utility-based agents Goals alone are not enough to generate high-quality behavior in most environments 30

31 Learning agents 31

32 END 32

Intelligent Agents. Outline. Agents. Agents and environments

Intelligent Agents. Outline. Agents. Agents and environments Outline Intelligent Agents Chapter 2 Source: AI: A Modern Approach, 2 nd Ed Stuart Russell and Peter Norvig Agents and environments Rationality (Performance measure, Environment, Actuators, Sensors) Environment