Ai in game programming it university of copenhagen Intelligent Agents Marco Loog.

ai in game programming it university of copenhagen

Intelligent Agents

Marco Loog


Some Unnecessary Remark

Book is rather formal and strict, we probably won’t be [in a lot of cases]


From Chapter 1

What is AI?

Two main considerations Thinking <> behavior Model humans <> ideal standard

Book is concerned with acting & ideals : acting rationally

[Good for games? Possible objections?]


More From Chapter 1

Acting rationally : rational agent

Agent : something that acts

Computer agent : operating under autonomous control, perceiving environment, adapting to change, capable of taking on another’s goal

Rational agent : acts so as to achieve the best [expected] outcome


And More...

Book deals with Intelligence that is concerned with rational

action General principles of rational agents and

their construction

Ideally, intelligent agent takes best possible action

However, the issue of limited rationality is also discussed


Some MoreUnnecessary Remarks

We will regularly talk about AI without necessarily referring to games and / or exemplifying everything based on games

Academia <> practice

This may ask a lot from the student’s creativity, autonomy, imagination, etc.


Intelligent Agents

We have determined what an intelligent agent is

In Chapter 2, agents, environments, and the interactions / relations between them are examined

Start out by a kind of taxonomy of possible agents and environments


Outline

Agents and environments

Rationality

PEAS

Environment types

Agent types


Agents and Environments

Agent : anything that can be viewed as perceiving its environment through sensors acting upon environment through actuators

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



Agent : anything that can be viewed as perceiving its environment through sensors acting upon environment through actuators

Percepts : agent’s perceptual inputs from the environment

Percept sequence : complete history of percepts

Generally, choice of action can depend on entire percept sequence



If possible to specify agent’s action for every percept sequence, then agent is fully defined

Agent’s complete behavior is described by this specification, which is called the agent function



If possible to specify agent’s action for every percept sequence, then agent is fully defined

Agent’s complete behavior is described by this specification, which is called the agent function

Mathematically, the agent function f maps percept histories P to actions A; f:P -> A Note that P’s cardinality is often infinite and

therefore tabulating it is impossible



Agent function is actually implemented via an agent program, which runs on the agent architecture

Agent program runs on the physical architecture to produce f

Agent := agent function + agent architecture


Schematically...


Vacuum-Cleaner World

Percepts : location and contents, e.g., [A,Dirty]

Actions: Left, Right, Suck, NoOp [do nothing]


A Vacuum-Cleaner Agent


?

What is the right way to fill out the previous percept sequence / action table?

What makes an agent good? What makes it bad?

How about intelligent?


Rational Agents

Agent should strive to “do the right thing” using What it can perceive The actions it can perform

Right action : one that will cause the agent to be most successful

Performance measure : objective criterion for measuring agent’s success


“General Rule”

It is better to design performance measures according to what one actually wants in the environment, rather than according to how one thinks the agent should behave

True? And for games? [FSM?]

One way or the other : this might be very difficult


For Our Vacuum-Cleaner Agent

Possible performance measures

Amount of dirt cleaned up? Amount of time taken? Amount of electricity consumed? Amount of noise generated? Etc.?


At the Risk of Somehow Repeating Myself Too Often...

What is rational for an agent depends on the following

Performance measure that defines success Prior knowledge of the environment Possible actions Percept sequence to date


Definition of 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, a priori knowledge the agent has


Some Necessary Remarks

Rationality is distinct from omniscience Agents can perform actions to modify future

percepts to obtain useful information [gathering / exploration]

An agent is autonomous if its behavior is determined by its own experience [with the ability to learn and adapt]

Initial agent configuration could reflect prior knowledge, but may be modified and augmented based on its experience

Let game agent adapt to new environment...


More : Agent Function

Successful agents split up the task of determining the agent function in three periods Design : certain perception / action relations

are directly available; f is partially pre-defined

‘In action’, while functioning : need for additional calculations before deciding on an action; right way to apply f

Learning from experience : mapping between percept sequence an actions is reconsidered; f is altered


And More : Autonomy

The more agent relies on prior knowledge of its designers, and the less on its percepts, the less autonomous it is

“A rational agent should be autonomous—it should learn what it can to compensate for partial or incorrect prior knowledge”

Do we agree?


Autonomy in Practice[?]

In practice, an agent may start with some prior knowledge + ability to learn

“Hence, the incorporation of learning allows one to design a single rational agent that will succeed in a vast variety of environments”


Task Environments

Before actually building rational agents, we should consider their environment

Task environment : the ‘problems’ to which rational agents are the ‘solutions’


Task Environment & PEAS

PEAS : Performance measure, Environment, Actuators, Sensors

Must first specify these setting for intelligent agent design


PEAS

Consider the task of designing an automated taxi driver Performance measure : safe, fast, legal,

comfortable trip, maximize profits Environment : roads, other traffic,

pedestrians, customers Actuators : steering wheel, accelerator,

brake, signal, horn Sensors : cameras, sonar, speedometer, GPS,

odometer, engine sensors, keyboard


Mo’ 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]


Mo’ 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] Agent's sensors give access to complete state of

the environment at each point in time Fully observable leads to cheating...

Deterministic [vs. stochastic] Next state of the environment is completely

determined by the current state and the action executed by the agent.

Strategic : deterministic except actions of other agents

Episodic [vs. sequential] Agent's experience is divided into atomic ‘episodes’

[perceiving and performing a single action]; choice of action in each episode depends only on the episode itself


Deterministic [vs. Stochastic]

In reality, situations are often so complex that they may are better treated as stochastic [even if they are deterministic]

[Luckily, we all like probability theory, statictics, etc., i.e., the appropriate tools for describing these environments]


Environment Types

Static [vs. dynamic] Environment is unchanged while an agent is

deliberating Semidynamic : environment itself not change

over time but agent’s performance score does Anyone playing chess? Clock -> semidynamic?

Discrete [vs. continuous] Limited number of distinct, clearly defined

percepts and actions Not clear-cut in practice

Single agent [vs. multi-agent] Agent operating by itself in an environment


Single [vs. Multi-Agent]

Some subtle issue

Which entities must be viewed as agents? The ones maximizing a performance measure, which depends on other agents

Competitive [vs. cooperative] environment

Pursuing goal of maximizing performance measure implies minimizing some other agent’s measure, e.g. as in chess


Environment Types [E.g]Chess with Chess without

Taxi a clock a clockdriving

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

Environment type largely determines agent design Real world is [of course] partially observable, stochastic,

sequential, dynamic, continuous, multi-agent Not always cut and dried / definition dependent Chess again... : castling? En passant capture? Draws by

repetition?


How About the Inside?

Earlier : mainly only gave description of agent’s behavior, i.e., action that is performed following any percept sequence, i.e., the agent function

Agent = architecture + program Job of AI : design agent program that

implements agent function Architectures is, more or less, assumed to be

given; in games [and other applications] design may take place simultaneously


Agent Functions and Programs

An agent is completely specified by the agent function mapping percept sequences to actions

One agent function [or a small equivalence class] is rational [= optimal]

Aim : find a way to implement the rational agent function concisely


Table-lookup Agent

Lookup table that relates every percept sequence to the appropriate action

Drawbacks Huge table [really huge in many cases] Take a long time to build the table [virtually

infinite in many cases] No autonomy Even with learning, need a long time to learn

the table entries

However, it does what it should do...


Key Challenge for AI

Find out how to write programs that, to the extent possible, produce rational behavior from a small amount of code [‘rules’] rather than from a large number of table entries


Four Basic Agent Types

Four basic types of agent programs embodying principles of almost every intelligent system

In order of increasing generality Simple reflex Model-based reflex Goal-based Utility-based


Simple Reflex

Agent selects action on the basis of current percept [if... then...]


Program for a Vacuum-Cleaner


From Simple Reflex to...

Simple reflex agents work only if the environment is fully observable, because decision should be made on the current percept


Model-Based Reflex

Effective way to handle partial observability is keeping track of every part of the environment the agent cannot see now

Some internal state should be maintained, which is based on the percept sequence, and which reflects some of the unobserved aspects of the current state


Internal State

Updating requires the encoding of two types of knowledge in the program

Knowledge about evolution of the world independent of the agent

How do own actions influence the environment?

Knowledge about world = model of world Model-based agent


Model-Based Reflex


From Model-Based Reflex to...

Knowledge about current state of the world not always enough

In addition, it may be necessary to define a goal / situation that is desirable


Goal-Based


From Goal-Based to...

For most environments, goals alone are not really enough to generate high-quality behavior

There are many ways in which one achieve a goal...

Instead, measure utility [‘hapiness’] Utility function maps states onto real

numbers describing degree of hapiness Can perform tradeoff if there are several ways to a

goal, or if there are multiple goals [using likelihood]


Utility-Based


Two Remarks on Utility

Rather theoretical result : any rational agent must behave as if it possesses a utility function whose expected value it tries to maximize

Utility-based agent programs are used let an agent handle inherent uncertainty in partially observable worlds


But How...

...do these agent programs come into existence?

The particular method suggested by Turing [the one from that machine] is to build learning agents, that should subsequently be teached

One of the advantages of these agents is that they can [learn to] deal with unknown environments


Learning Agent

Four conceptual components Learning element : responsible for making

improvements Performance element : responsible for

external actions [previously considered equal to entire agent]

Critic : provides feedback on how agent is doing and determines how performance element should be modified

Problem generator : responsible for suggesting actions leading to new and informative experience


Learning Agent


Next Week

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Ai in game programming it university of copenhagen Intelligent Agents Marco Loog.

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