MULUNGUSHI UNIVERSITY
Pursing frontiers of Knowledge

CENTRE FOR ICT EDUCATION
ICT 352 Artificial Intelligence and Prolog
Topic: Intelligent Agents
Lecturer: M. Simfukwe
Monday, 20 May, 2013

Outline
• Agents and environments
• Rationality
• PEAS (Performance measure,
Environment, Actuators, Sensors)
• Environment types
• Agent types

Agents
• An agent is any system that gets in some input and processes it to get some output.
• Software agents in classical computer science • Hardware agents (robots), with sensors and actuators.

Agents
• An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators
• Percept refers to agents perceptual inputs
• Percept sequence: Complete history of everything the agent has perceived and stored in KB.
• Performance Measure: Criterion for success for agent.

Agents (cont..)
• Rationality: determined by 4 factors
1.performance measure
2.prior knowledge of environment
3.actions that the agent can perform
4.percept sequence to date.

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 percept sequence and whatever built-in knowledge the agent has.

Agents and environments

• The agent function maps from percept histories to actions:
[f: P* A]

Vacuum-cleaner world

• Percepts: location and contents, e.g.,
[A,Dirty]
• Actions: Left, Right, Suck, NoOp

Vacuum-cleaner world

PEAS
• PEAS: Performance measure,
Environment, Actuators, Sensors
• Consider, e.g., the task of designing an automated taxi driver: o o o o

Performance measure
Environment
Actuators
Sensors

PEAS
• Must first specify the setting for an intelligent agent design
• Consider, e.g., the task of designing an automated taxi driver:
Performance measure: Safe, fast, legal, comfortable trip, maximize profits o Environment: Roads, other traffic, pedestrians, customers o Actuators: Steering wheel, accelerator, brake, signal, horn o Sensors: Cameras, sonar, speedometer, GPS, odometer, engine sensors, keyboard o PEAS
• Agent: Medical diagnosis system
• Performance measure: Healthy patient, minimize costs, lawsuits
• Environment: Patient, hospital, staff
• Actuators: Screen display (questions, tests, diagnoses, treatments, referrals)
• Sensors: Keyboard (entry of symptoms, findings, patient's answers)

PEAS
• Agent: Part-picking robot
• Performance measure: Percentage of parts in correct bins
• Environment: Conveyor belt with parts, bins • Actuators: Jointed arm and hand
• Sensors: Camera, joint angle sensors

PEAS
• Agent: Interactive English tutor
• Performance measure: Maximize student's score on test
• Environment: Set of students
• Actuators: Screen display (exercises, suggestions, corrections)
• Sensors: Keyboard

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. • 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), and the choice of action in each episode depends only on the episode itself.

Environment types
• Static (vs. dynamic): The environment is unchanged while an agent is deliberating.
• 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.
• Known (vs. unknown): When the outcomes/outcome probabilities for all actions are given.

Environment types
• The environment type largely determines the agent design
• The real world is (of course) partially observable, stochastic, sequential, dynamic, continuous, multi-agent

Agent types
• Four basic types in order of increasing generality: • Simple reflex agents
• Model-based reflex agents / Agent that keep track of the world.
• Goal-based agents
• Utility-based agents

Simple reflex agents
• The agents select actions on the basis of the current percept, ignoring the rest of the percept history. • The agents tend to be very simple and exhibit limited intelligence.
• Since all actions are based on the current percept, the environment needs to be fully observable for a correct decision to be made.

Simple reflex agents

Simple reflex agents

Model-based reflex agents
• The most effective way of handling partial observability is to keep track of the part of the world that cant be seen.
• The agents needs to maintain an internal state that depends on the percept history and thereby reflects some of the unobserved aspects of the current state.
To update the internal state information as time passes, two knowledge kinds need to be encoded in the knowledge program. 1. Information on how the world evolves independent of the agent
2. How the agents action affect the world

Model-based reflex agents

Goal-based agent
• Knowledge of the environment’s current state is not always enough for decision making
• A goal s needed to determine the desirable situations Goal-based agents

Utility-based agents
• Goals alone are no enough to generate highquality behaviour in most environments
• They provide a crude binary distinction between “happy” and “unhappy” state.
• Trade off specification when there are conflicting goals
• When there are several goals, needed to be met Utility-based agents

Learning agents
• Allow the agent to operate in initially unknown environments and exceed the competence accorded by it’s initial knowledge. • Learning element: responsible for making improvements • Performance element: handles selection of external actions.

Learning agents
• Critic: provides feedback to the learning element on the agent’s performance and determines how the performance element should be modified to do better in future.
• Problem generator: suggests actions that leads to new and informative experiences.

Learning agents

Read chapter 2!!!!!