do software agents know what they talk about?

Do software agents know what they talk about?

Agents and Ontology

dr. Patrick De Causmaecker, Nottingham, March 7-11 2005

Deductive reasoning agents

Nottingham, March 2005

Agents and Ontology Patrick.DeCausmaecker@kahosl.be

Logical programming First order logic Example: Prolog Example: Rule based systems Example: Constraint Satisfaction

First order logic

Predicates on atoms, not on predicates. Quantifiers relate atoms Grelling’s paradox (cannot be

expressed in first order logic)

If an adjective truly describes itself, call it “autological", otherwise call it "heterological". For example, "polysyllabic" and "English" are autological, while "monosyllabic" and "pulchritudinous" are heterological. Is "heterological" heterological? If it is, then it isn't; if it isn't, then it is.”

Example: Prolog

http://www.ugosweb.com/jiprolog/

Nottingham, March 2005 Agents and Ontology Patrick.DeCausmaecker@kahosl.be

father(terach,abraham).father(terach,nachor).father(terach,haran).father(abraham,isaac).father(haran,lot):-!.father(haran,milcah).mother(sara,isaac).male(terach).male(abraham).male(nachor).male(haran).male(isaac).male(lot).female(sarah).female(milcah).female(yiscah).likes(X,pome).son(X,Y):-father(Y,X),male(X).daughter(X,Z):-father(Z,X),female(X).granfather(X,Z):-father(X,Y),father(Y,Z).

hanoi(1, A,B,C,[[A,B]]):-!.

hanoi(N, A,B,C,Moves):- N1 is N - 1, hanoi(N1, A,C,B,Ms1), hanoi(N1, C,B,A,Ms2), append(Ms1, [[A,B]|Ms2], Moves), !.

Towers of Hannoi

Example: Rulebased systems

http://www.expertise2go.com/download/demo.html

RULE [Is the battery dead?]If [the result of switching on the headlights] = "nothing happens" or[the result of trying the starter] = "nothing happens"Then [the recommended action] = "recharge or replace the battery"

RULE [Is the car out of gas?]If [the gas tank] = "empty"Then [the recommended action] = "refuel the car"

RULE [Is the battery weak?]

If [the result of trying the starter] : "the car cranks slowly" "the car cranks normally" and

[the headlights dim when trying the starter] = true and

[the amount you are willing to spend on repairs] > 24.99

Then [the recommended action] = "recharge or replace the battery"

RULE [Is the car flooded?]

If [the result of trying the starter] = "the car cranks normally" and

[a gas smell] = "present when trying the starter"

Then [the recommended action] = "wait 10 minutes, then restart flooded car"

RULE [Is the gas tank empty?]

If [the result of trying the starter] = "the car cranks normally" and

[a gas smell] = "not present when trying the starter"

Then [the gas tank] = "empty" @ 90

PROMPT [the result of trying the starter] Choice CF

"What happens when you turn the key to try to start the car?"

"the car cranks normally"

"the car cranks slowly"

"nothing happens"

PROMPT [a gas smell] MultChoice CF

"The smell of gasoline is:"

"present when trying the starter"

"not present when trying the starter"

PROMPT [the result of switching on the headlights] MultChoice CF

"The result of switching on the headlights is:"

"they light up"

"nothing happens"

PROMPT [the headlights dim when trying the starter] YesNo CF

"Do the headlights dim when you try the starter with the lights on?"

Example: Constraint Satisfactionhttp://kti.ms.mff.cuni.cz/~bartak/constraints/

index.html

Deductive reasoning Intelligent behaviour can be reached

by providing the system with a symbolic representation of its environment and allow it to manipulate this representation syntactically

The symbolic representation is a set of logical formulas. The manipulation is deduction, or theorem proving.

Interp:Pixel manipulation

Knowledge bank: belief:dist(mij,d1) = 90 cmdoor(d1)

PlanSTOP

ActionBREAK!

Two problems Transduction

Sufficiantly fast transformation of observations in an adequate symbolic representation.

Representation/reasoning The symbolic representation as a

basis for the manipulation process. Both should be sufficiently fast.

AI aproach Perception:

Vision, speach, natural language, learning,…

Representation Knowledge representation tasks,

automatic reasoning, automatic planning A lot of work has been done, results

are still very limited.

Agents as theorem provers The internal state of the agent is a

database of first order predicates:

This database contains all beliefs of the agent.

Open(valve221)Temperature(reactor4726,321)Pressure(tank776,28)

Agents as theorem provers Beliefs are not exact, complete. Interpretation may be faulty. Still these predicates are all the

agent can walk on.

Agents as theorem provers FormallyL = {all first-order predikaten}D = (L) = {all L databases}, 1, 2,… D= {deductionrules of the agent} means that formula from L can be proven from database using rules .

Agents as theorem provers The agent:

The perception function: see : S -> Per

The adaptation of the internal state: next : D Per -> D

The action function: action : D -> Ac

Function Action by proof1. Function action( :D) return een actie Ac2. begin3. for each Ac4. if Do() then return 5. end for6. for each Ac7. if Do() then return 8. end for9. return null10. end

Example: the vacuum cleaning agent

Vacuum cleaning The world

Previous information changes

old() = {P(t1,…,tn) |P {In,Dirt,Facing} en P(t1,…,tn) }}

In(x,y)Dirt(x,y)Facing(d)

Vacuum cleaning The function new generates new

knowledge: new : D Per -> D (exercise)

One can define next as: next(,p) = ( \old()) new(,p)

Vacuum cleaning Deductionrules are as

(…) (…) “If is consistent with the content of the

database, conclude ” Rule 1: arbeit

In(x,y) Dirt(x,y) Do(suck) Rule 2:bewegen

In(0,0) Facing(north) Dirt(0,0) Do(forward) In(0,1) Facing(north) Dirt(0,0) Do(forward) In(0,2) Facing(north) Dirt(0,0) Do(turn) In(0,2) Facing(east) Do(forward)

Conclusions Rather impractical… Agent must try do determine its optimal

action by reasoning. This takes time (deductive systems are

slow). The world can have changed… “calculative rationality”: agent decides

for the optimal action at the time of the start of the reasoning process.

Not allways acceptable

Other problems Logic is elegant but slow The see functie is in a difficult,

poorly understood, sector of AI. The vacuum cleaning problem was

already difficult to describe!

Agent georiënterd programming: Agent0 (Shoham 1993) Desire, belief, intention In Agent0 an agent is

capabilities, Initial beliefs Initial commitments Rules to deduct commitments (commitment

rules).

Agent0 A commitment rule is

A message condition To be compared with received messages

A mentale condition To be compared with the beliefs and

intentions An action actie

To be selected if appropriate

Agent0 Two kinds of actions:

Communicative Private

Three kinds of messages: Requests for action Unrequests to stop action Inform for infomation

COMMIT((agent, REQUEST, DO(time, action)) ;;; boodschapvoorwaarde

(B,[now, Friend agent] AND CAN(self, action) AND NOT [time, CMT(self, anyaction)]), ;;; mentale voorwaarde

self, DO(time,action))

Initialize

Update beliefs

Update commitments

Execute

Beliefs

Commitments

Abilities

messages in

messages outinternal actions

do software agents know what they talk about?

ontology patrick

reasoning agents agents

gas tank

software agents

anda gas smell

recommended action

heterological heterological

orthe result

Documents

talk 8 new biologic agents in ln - richard j. glassock

physical agents. objectives: to know the definition of...

5 agents of change investors need to know about now ·...

charlie talk - five plugins you ought to know about

what agents need to know about the cadillac tax - generic -...

what real estate agents/brokers need to know: “know before...

walking the talk on climate change banks as agents of...

do software agents know what they talk about? agents and...

what does god know? supernatural agents’ access to...

what do you know about text talk?

fha va loans what agents need to know

know your rights as a refugee in the...

accounting for real estate agents - aspire wealth group ·...

the things that you need to know about agents. session...

agents that talk and hit back: animated agents in

how to talk to women you don’t know ?

apre 2012: talk to me like you know me

i don’t know what to call this talk

tech talk: getting to know node.js

annual lecture heterogeneous agents, social interactions and...