Do software agents know what they talk about?
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Transcript of 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 [email protected]
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Logical programming First order logic Example: Prolog Example: Rule based systems Example: Constraint Satisfaction
Nottingham, March 2005
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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.”
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Example: Prolog
http://www.ugosweb.com/jiprolog/
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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).
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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
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Example: Rulebased systems
http://www.expertise2go.com/download/demo.html
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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"
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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"
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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"
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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
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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"
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PROMPT [a gas smell] MultChoice CF
"The smell of gasoline is:"
"present when trying the starter"
"not present when trying the starter"
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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?"
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Example: Constraint Satisfactionhttp://kti.ms.mff.cuni.cz/~bartak/constraints/
index.html
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Nottingham, March 2005 Agents and Ontology [email protected]
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Nottingham, March 2005 Agents and Ontology [email protected]
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Nottingham, March 2005 Agents and Ontology [email protected]
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Nottingham, March 2005
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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.
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Interp:Pixel manipulation
Knowledge bank: belief:dist(mij,d1) = 90 cmdoor(d1)
PlanSTOP
ActionBREAK!
D020
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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.
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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.
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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)
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Agents as theorem provers Beliefs are not exact, complete. Interpretation may be faulty. Still these predicates are all the
agent can walk on.
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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 .
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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
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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
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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)
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Vacuum cleaning The function new generates new
knowledge: new : D Per -> D (exercise)
One can define next as: next(,p) = ( \old()) new(,p)
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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)
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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
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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!
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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).
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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
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Agent0 Two kinds of actions:
Communicative Private
Three kinds of messages: Requests for action Unrequests to stop action Inform for infomation
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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))
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Initialize
Update beliefs
Update commitments
Execute
Beliefs
Commitments
Abilities
messages in
messages outinternal actions