Agents

Agents• An agent is anything that can be viewed as perceiving

its environment through sensors and acting upon that environment through actuators.

• So an agent as a system maps percept sequence to actions.

• An agent may have goals.• A performance measure is required to evaluate an

agent.

• 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

Agents and environments

• The agent function maps from percept histories to actions:

[f: P* A]• The agent program runs on the physical architecture

to produce f• agent = architecture + program

Example :Vacuum-cleaner world

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

• input{tables/vacuum-agent-function-table}

Sensors and EffectorsAn agent perceives its environment through its

sensors.The complete set of inputs at a given time is called

percepts.It can also be a sequence of percepts.

By using effectors the environment can be changed.These are called as actuators and there actions are

called as actions.

PerformanceBehavior and performance of an agent with respect to

agent actions.Mapping between Perception history to action mappingIdeal mapping: Specifies which action an agent should take

at any time.

Performance measure: A subjective measure to characterize how successful an agent is.Parameters : speed, time, money, accuracy

• Autonomous Agent: Specifies which action to take in current situation to maximize its progress toward goal.

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. Example Chess and Bridge Game.

• Deterministic (vs. stochastic): The next state of the environment is completely determined by the current state and the action executed by the agent. Example chess A deterministic but partially observable environment is

Stochastic. Example Robot, Ludo If the environment is deterministic except for the actions of

other agents, then the environment is strategic. E.g Chess

• 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. In sequential environment, the agent engages in series of

connected episode's.

• 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.

A Dynamic Environment changes over time independent of the actions of agent.

• 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.

• Complex Environment: The environment which is: Knowledge rich, The environment has enormous

amount of knowledge. Input rich, The environment has huge amount of

inputs.

The agent must have a way of managing these complexity.Should have good sensing strategies and attention

mechanism.

Example of Environment typesChess with Chess without Taxi driving a clock a clock

Fully observable Yes Yes No Deterministic Strategic Strategic No Episodic No No No Static Semi Yes No Discrete Yes Yes NoSingle agent No No No

• The environment type largely determines the agent design.• The real world is (of course) partially observable, stochastic,

sequential, dynamic, continuous, multi-agent.

Agent Architecture

• 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

Types of agents

Rational agents• An agent should strive to "do the right thing", based on what it can

perceive and the actions it can perform. 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 behaviorE.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.

• So a Rational Agent, for each possible percept sequence, it 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.

• Perfect Rationality:Assumes that Rational agent knows all and will take action to

maximize its utility.Human beings-No.

• Bounded Rational (Herbert Simon , 1972)Limited Rationality

Human mind

Is Rational = BestYes to the best of its knowledge

Is Rational=OptimalYes to the best of its abilities and constraints.

• Rationality is distinct from omniscience (all-knowing with infinite knowledge).Rational agent does not know the actual outcome of its actions.May not know certain aspect of its actions.

• Rational Agents must also take into account its limitations.

Table-lookup agent

• input{algorithms/table-agent-algorithm}

• 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.

Percept based agent

• Information comes from sensors-percepts.• Changes the agents current state of world.• Triggers actions through affecters.

• Reactive agents and stimulus based agents.

• No notion of history the current state is as the sensors see it right now.

Simple reflex agents

Goal-based agents

•

Utility-based agents

Learning agents

What is an intelligent agentWhat is an intelligent agent

IntelligentAgent

user/environment

output/

sensors

effectors

input/

An intelligent agent is a system that:

• perceives its environment (which may be the physical world, a user via a graphical user interface, a collection of other agents, the Internet, or other complex environment);

• reasons to interpret perceptions, draw inferences, solve problems, and determine actions; and

• acts upon that environment to realize a set of goals or tasks for which it was designed.•Performance measure: to make subjective measure of the performance.

Humans, with multiple, conflicting drives, multiple senses, multiple possible actions, and complex sophisticated control structures, are at the highest end of being an agent.

At the low end of being an agent is a thermostat.It continuously senses the room temperature, starting or stopping the heating system each time the current temperature is out of a pre-defined range.

The intelligent agents we are concerned with are in between. They are clearly not as capable as humans, but they are significantly more capable than a thermostat.

What is an intelligent agent (cont.)What is an intelligent agent (cont.)

An intelligent agent

• interacts with a human or some other agents via some kind of agent-communication language

• may not blindly obey commands, but may have the ability to:- modify requests,- ask clarification questions, or - even refuse to satisfy certain requests.

What is an intelligent agent (cont.)What is an intelligent agent (cont.)

An intelligent agent

• can accept high-level requests indicating what the user wants

• can decide how to satisfy each request:- with some degree of independence or autonomy, - exhibiting goal-directed behavior and - dynamically choosing which actions to take, and in what sequence.

What is an intelligent agent (cont.)What is an intelligent agent (cont.)

An intelligent agent can :

• collaborate with its user to improve the accomplishment of his or her tasks;

• carry out tasks on user’s behalf, and in doing so employs some knowledge of the user's goals or desires;

• monitor events or procedures for the user;

• advise the user on how to perform a task;

• train or teach the user;

• help different users collaborate.

What an intelligent agent can doWhat an intelligent agent can do

The ability to improve its competence and efficiency.

An agent is improving its competence if it learns to solve a broader class of problems, and to make fewer mistakes in problem solving.

An agent is improving its efficiency if it learns to solve more efficiently (for instance, by using less time or space resources) the problems from its area of competence.

Ability to learnAbility to learn

Extended agent architectureExtended agent architecture

Ontology

Rules/Cases/Methods

Problem SolvingEngine

Intelligent Agent

User/Environment Output/

Sensors

Effectors

Input/

Knowledge Base

LearningEngine

The learning engine implements methods for extending and refining the knowledge

in the knowledge base.

How are agents builtHow are agents built

A knowledge engineer attempts to understand how a subject matter expert, reasons and solves problems and then encodes the acquired expertise into the agent's knowledge base.

The expert analyzes the solutions generated by the agent (and often the knowledge base itself) to identify errors, and the knowledge engineer corrects the knowledge base.

KnowledgeEngineer

DomainExpert

Knowledge Base

Inference Engine

Intelligent Agent

ProgrammingDialog

Results

Why it is hardWhy it is hard

The knowledge engineer has to become a kind of subject matter expert in order to properly understand expert’s problem solving knowledge. This takes time and effort.

Experts express their knowledge informally, using natural language, visual representations and common sense, often omitting essential details that are considered obvious. This form of knowledge is very different from the one in which knowledge has to be represented in the knowledge base (which is formal, precise, and complete).

This transfer and transformation of knowledge, from the domain expert through the knowledge engineer to the agent, is long, painful and inefficient (and is known as "the knowledge acquisition bottleneck“ of the AI systems development process).

Why are intelligent agents importantWhy are intelligent agents important

Humans have limitations that agents may alleviate (e.g. memory for the details that isn’t effected by stress, fatigue or time constraints).

Humans and agents could engage in mixed-initiative problem solving that takes advantage of their complementary strengths and reasoning styles.

Why are intelligent agents important (cont)Why are intelligent agents important (cont)

The evolution of information technology makes intelligent agents essential components of our future systems and organizations.

Our future computers and most of the other systems and tools will gradually become intelligent agents.

We have to be able to deal with intelligent agents either as users, or as developers, or as both.