Do software agents know what they talk about?

1. Do software agents know what they talk about? Agents and Ontology dr. Patrick De Causmaecker, Nottingham, March 7-11 2005

2. Deductive reasoning agents

3. Logical programming • First order logic • Example: Prolog • Example: Rule based systems • Example: Constraint Satisfaction Agents and Ontology Patrick.DeCausmaecker@kahosl.be

4. 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.” Agents and Ontology Patrick.DeCausmaecker@kahosl.be

5. Example: Prolog • http://www.ugosweb.com/jiprolog/ Agents and Ontology Patrick.DeCausmaecker@kahosl.be

6. 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). Agents and Ontology Patrick.DeCausmaecker@kahosl.be

7. Towers of Hannoi 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), !. Agents and Ontology Patrick.DeCausmaecker@kahosl.be

8. Example: Rulebased systems http://www.expertise2go.com/download/demo.html Agents and Ontology Patrick.DeCausmaecker@kahosl.be

9. 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" Agents and Ontology Patrick.DeCausmaecker@kahosl.be

10. 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" Agents and Ontology Patrick.DeCausmaecker@kahosl.be

11. 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" Agents and Ontology Patrick.DeCausmaecker@kahosl.be

12. 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 Agents and Ontology Patrick.DeCausmaecker@kahosl.be

13. 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" Agents and Ontology Patrick.DeCausmaecker@kahosl.be

14. PROMPT [a gas smell] MultChoice CF "The smell of gasoline is:" "present when trying the starter" "not present when trying the starter" Agents and Ontology Patrick.DeCausmaecker@kahosl.be

15. 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?" Agents and Ontology Patrick.DeCausmaecker@kahosl.be

16. Example: Constraint Satisfaction http://kti.ms.mff.cuni.cz/~bartak/constraints/index.html Agents and Ontology Patrick.DeCausmaecker@kahosl.be

17. Agents and Ontology Patrick.DeCausmaecker@kahosl.be

18. Agents and Ontology Patrick.DeCausmaecker@kahosl.be

19. Agents and Ontology Patrick.DeCausmaecker@kahosl.be

20. Agents and Ontology Patrick.DeCausmaecker@kahosl.be

21. 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. Agents and Ontology Patrick.DeCausmaecker@kahosl.be

22. Interp: Pixel manipulation • Knowledge bank: belief: • dist(mij,d1) = 90 cm • door(d1) D020 Plan STOP Action BREAK! Agents and Ontology Patrick.DeCausmaecker@kahosl.be

23. 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. Agents and Ontology Patrick.DeCausmaecker@kahosl.be

24. 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 and Ontology Patrick.DeCausmaecker@kahosl.be

25. 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 and Ontology Patrick.DeCausmaecker@kahosl.be

26. Agents as theorem provers • Beliefs are not exact, complete. • Interpretation may be faulty. • Still these predicates are all the agent can walk on. Agents and Ontology Patrick.DeCausmaecker@kahosl.be

27. Agents as theorem provers • Formally • L = {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 and Ontology Patrick.DeCausmaecker@kahosl.be

28. 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 Agents and Ontology Patrick.DeCausmaecker@kahosl.be

29. Function Action by proof • Function action( :D) return een actie Ac • begin • for each   Ac • if  Do() then return  • end for • for each   Ac • if  Do() then return  • end for • return null • end Agents and Ontology Patrick.DeCausmaecker@kahosl.be

30. Example: the vacuum cleaning agent Agents and Ontology Patrick.DeCausmaecker@kahosl.be

31. Vacuum cleaning • The world • Previous information changes In(x,y) Dirt(x,y) Facing(d) old() = {P(t1,…,tn) |P  {In,Dirt,Facing} en P(t1,…,tn)  }} Agents and Ontology Patrick.DeCausmaecker@kahosl.be

32. Vacuum cleaning • The function new generates new knowledge: • new : D  Per -> D (exercise) • One can define next as: • next(,p) = ( \old())  new(,p) Agents and Ontology Patrick.DeCausmaecker@kahosl.be

33. 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) Agents and Ontology Patrick.DeCausmaecker@kahosl.be

34. 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 Agents and Ontology Patrick.DeCausmaecker@kahosl.be

35. 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! Agents and Ontology Patrick.DeCausmaecker@kahosl.be

36. 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). Agents and Ontology Patrick.DeCausmaecker@kahosl.be

37. 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 Agents and Ontology Patrick.DeCausmaecker@kahosl.be

38. Agent0 • Two kinds of actions: • Communicative • Private • Three kinds of messages: • Requests for action • Unrequests to stop action • Inform for infomation Agents and Ontology Patrick.DeCausmaecker@kahosl.be

39. 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) ) Agents and Ontology Patrick.DeCausmaecker@kahosl.be

40. messages in Initialize Beliefs Update beliefs Commitments Update commitments Abilities Execute messages out internal actions Agents and Ontology Patrick.DeCausmaecker@kahosl.be