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Intelligent AgentsCSL 302 ARTIFICIAL INTELLIGENCE

SPRING 2014

Definition of AI

1/17/2014 CSL 302 ARTIFICIAL INTELLIGENCE, INDIAN INSTITUTE OF TECHNOLOGY ROPAR 2

Acting Rationally

rational behavior = doing the right thing

Encompasses the other lines of thought.oThinking rationally will help to act rationally, but is not the

only means; Eg: Reflex

Goal: building rational agents

Thinking humanly Thinking rationally

Acting humanly Acting rationally

AgentEnvironment

Agent

Perc

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ction

What should I do next?

1/17/2014 3CSL 302 ARTIFICIAL INTELLIGENCE, INDIAN INSTITUTE OF TECHNOLOGY ROPAR

Agent Functions and ProgramAgent behavior is described by the agent function that maps a percept sequences to actions.

Lookup Table – An action for every possible percept sequence.

Agent Program: realization/concrete implementation of the agent function within some physical system.


Vacuum World


Rational AgentsA rational agent does the right thing(action)

Without loss of generality, “goals” specifiable by performance measure defining a numerical value for any environment history

Rational Action: that maximizes the expected value of the performance measure given the percept sequence to data and prior knowledge

Rationality ≠ Omniscience

Rationality ≠ Successful

Rationality ≠ Clairvoyant

Rationality ≠ Intentionally no Sensing


PEAS – Specifying the Task EnvironmentMust specify the task environment as fully as possible

oPerformance

oEnvironment

oActuator

oSensors

Task Environment for automated taxi driver?



oPerformance- safe, fast, comfortable

oEnvironment-roads, other traffic, traffic signals

oActuator-steering, accelerator, brake, horn, signal

oSensors-video camera, IR sensor, GPS, odometer

Task Environment for automated taxi driver?


PEAS – Specifying the Task EnvironmentHow does the following affect the complexity of the problem the rational agent faces?

oPerformance – complex goals makes performance harder to achieve?

oEnvironment

oActuator – Lack of effectors makes performance harder to achieve?

oSensors – Lack of percepts makes performance harder to achieve?


Intelligent Agents17/1


Homework 1 is due on Monday 20-1-2014.


oPerformance

oEnvironment

oActuator

oSensors


Properties of the Task Environment

Environment

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What should I do next?

Static vs. DynamicPartially vs. Fully Observable

Deterministic vs. Stochastic

Instantaneousvs. Durative

Full vs. Partial Satisfaction

Discrete vs. Continuous

Single vs. Multiple Agents

Episodic vs. Sequential


Properties of the Task Environment


Observable: The agent can “sense” its environment

obest: fully observable worst: unobservable typical: partially observable

Deterministic: The actions have predictable effects

obest: deterministic worst: non-deterministic typical: stochastic

Static: The world evolves only because of agents’ actions

obest: static worst: dynamic typical: quasi-static

Episodic: The performance of the agent is determined episodically

obest: episodic worst: non-episodic

Discrete: The environment evolves through a discrete set of states

obest: discrete worst: continuous typical: hybrid

Agents: # of agents in the environment; are they competing or cooperating?

Task Environment-ExamplesEnvironment Observable Deterministic Static Episodic Discrete # Agents

Chess



Chess Fully Deterministic Semi Sequential Discrete Multi




Poker




Poker Partial Stochastic Static Sequential Discrete Multi




Poker Partially Stochastic Static Sequential Discrete Multi

Taxi Driving





Taxi Driving Partially Stochastic Dynamic Sequential Continuous Multi






M-Diagnosis






M-Diagnosis Partially Stochastic Dynamic Sequential Continuous Single







I-Analysis







I-Analysis Fully Deterministic Semi Episodic Continuous Single








Inter. Tutor








Inter. Tutor Partially Stochastic Dynamic Sequential Discrete Multi








Inter. Tutor Partially Stochastic Dynamic Sequential Discrete Multi


The real world is inaccessible, stochastic, dynamic and continuousHow do we handle it then?

Types of AgentsTypes of agents (increasing in generality and ability to handle complex environments)oSimple reflex agents

oModel based reflex agents

oGoal-based agents

oUtility-based agents

oLearning agents


Simple Reflex Agents


Model Based Reflex Agents


State Estimation

Goal Based Agents


State Estimation

Search/Planning

Search: process of looking for a sequence of actions that reaches the goal statePlanning: can be viewed as search in a structured environment.

Utility Based Agents


• Utility function: internalization of the performance measure• Conflicting goals• Multiple uncertain goals• Decision theoretic planning

Learning Agents


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