1. A Logic Programming Based Software Architecture for Reactive Intelligent Mobile Agents - DIPLCL’99 Copyright © 1999, BinNet Corp. Paul Tarau University of North Texas & BinNet Corporation

2. Summary –Introduction/Motivation –Kernel Prolog = Horn Clauses + Engines –Threads as First-Order Objects –Blackboard Operations –Mobile Threads and Remote Execution –Key Agent Programming Patterns –Case Studies, Examples, Applications –Conclusion

3. Motivation –paradigm shift towards networked, mobile, ubiquitous computing with increasingly complex patterns of interaction –threads: needed for programming reactive/proactive mobile agents –back to pure LP - a good thing - but let’s give it an expressiveness lift:-) –Hilbert’s approach => reusable design patterns through an axiomatic reconstruction of frequently used components!

4. Ontology Places: blackboards + a server thread listening on a portPlaces: blackboards + a server thread listening on a port Things: Prolog terms, in particular Prolog clausesThings: Prolog terms, in particular Prolog clauses Agents:Agents: –a set of mobile threads initiated by a unique goal at a given Place –coordination: through blackboards - some local, some remote

5. Orthogonal Execution Mechanisms enginesengines threadsthreads blackboard operationsblackboard operations –basic Linda coordination, associative search –blackboard constraints - –blackboard constraints - react when something becomes true mobile threads operations

6. Engines as Generalized Iterators (Fluents): keep state minimal Engine = an LD Resolution Interpreter with first order control: constructor+iterator –new_engine(Goal,AnswerPattern, Handle): creates a new interpreter Handle solving Goal –new_answer(Handle,AnswerInstance): If AnswerInstance is no then stops the engine, otherwise either returns a new answer, of the form the(AnswerInstance) or returns no if there are no more answers.

7. findall/3 –findall(X,G,Xs):- new_engine(G,X,E), new_answer(E,Answer), collect_all_answers(Answer,E,Xs). –collect_all_answers(no,_,[]). collect_all_answers(the(X),E,[X|Xs]):- new_answer(E,Answer), collect_all_answers(Answer,E,Xs).

8. Reflective Meta Interpreter solve(G):- once(reduce(G,NewG)), call(NewG).solve(G):- once(reduce(G,NewG)), call(NewG). reduce(G,G):-is_builtin(G). reduce(','(A,B),','(solve(A),solve(B))). reduce(G,','(clause(G,Gs),solve(Gs))).reduce(G,G):-is_builtin(G). reduce(','(A,B),','(solve(A),solve(B))). reduce(G,','(clause(G,Gs),solve(Gs))).

9. Thread Operations: minimal! bg(Goal,ThreadHandle): runs a thread initiated by Goal and returns a handlebg(Goal,ThreadHandle): runs a thread initiated by Goal and returns a handle thread_clone(CloneThreadHandle)thread_clone(CloneThreadHandle) thread_join(T): wait for T to finishthread_join(T): wait for T to finish thread_sleep(Duration), thread_this(ThisThreadHandle)thread_sleep(Duration), thread_this(ThisThreadHandle) thread_resume/1, thread_suspend/1thread_resume/1, thread_suspend/1

10. Basic Linda Operations out(X): puts X on the blackboardout(X): puts X on the blackboard in(X): waits until it can take an object matching X from the blackboardin(X): waits until it can take an object matching X from the blackboard all(X,Xs): reads the list Xs matching X currently on the blackboardall(X,Xs): reads the list Xs matching X currently on the blackboard derived operations: cin/1, rd/1derived operations: cin/1, rd/1

11. Implementing in/1, out/1 with suspend and resume in Jinni and BinProlog this is in the implementation language but, in fact, it could be at source levelin Jinni and BinProlog this is in the implementation language but, in fact, it could be at source level in(X): if X is on the blackboard take it, if not add waiting(X,ThisThread) and do suspend(ThisThread)in(X): if X is on the blackboard take it, if not add waiting(X,ThisThread) and do suspend(ThisThread) out(X): if waiting(X,T) matches, take it and call resume(T)out(X): if waiting(X,T) matches, take it and call resume(T)

12. Assertional Constraints vs. Binding Constrains From: when nonvar(X)...From: when nonvar(X)... => when provable(X) … re-execution=> when provable(X) … re-execution => when a_fact(X)… more realistic=> when a_fact(X)… more realistic constraints on variable bindings are too fine-grained for distributed programming!constraints on variable bindings are too fine-grained for distributed programming!

13. Beyond Linda: Blackboard Constraint Operations wait_for(Pattern,Constraint): waits for a Pattern on the blackboard, such that Constraint holds, and when this happens, it removes the result of the match from the blackboardwait_for(Pattern,Constraint): waits for a Pattern on the blackboard, such that Constraint holds, and when this happens, it removes the result of the match from the blackboard notify_about(Pattern): notifies about this Pattern one of the blocked threads which waits for it with a matching constraintnotify_about(Pattern): notifies about this Pattern one of the blocked threads which waits for it with a matching constraint

14. Coordination with Blackboard Constraints Two threads: T1 (prod) and T2 (cons)Two threads: T1 (prod) and T2 (cons) ?-notify_about(stock_offer(aol,91)). %T1?-notify_about(stock_offer(aol,91)). %T1 ?-notify_about(stock_offer(aol,89)). %T1?-notify_about(stock_offer(aol,89)). %T1 % action triggered in T2 => Price=89% action triggered in T2 => Price=89 ?-wait_for(stock_offer(aol,Price), %T2 less(Price,90)).?-wait_for(stock_offer(aol,Price), %T2 less(Price,90)).

15. wait_for(Pattern,Constr) wait_for(P,C):- if(take_pattern(available_for(P),C), if(take_pattern(available_for(P),C), true, true, and(out(waiting_for(P,C)), and(out(waiting_for(P,C)), in(holds_for(P,C)) in(holds_for(P,C)) ) ). ) ).

16. notify_about(Pattern,Constr) notify_about(P):- if(take_pattern(waiting_for(P,C),C), if(take_pattern(waiting_for(P,C),C), out(holds_for(P,C)), out(holds_for(P,C)), out(available_for(P)) out(available_for(P)) ). ).

17. take_pattern/2 take_pattern(Pattern,Constraint):- all(Pattern,Ps), all(Pattern,Ps), member(Pattern,Ps), member(Pattern,Ps), Constraint, Constraint, cin(Pattern,_). % non-blocking in/1 cin(Pattern,_). % non-blocking in/1

18. The Reactive Agent Pattern sell(Who,Stock,AskPrice) :- notify_about(offer(Who,Stock,AskPrice)).sell(Who,Stock,AskPrice) :- notify_about(offer(Who,Stock,AskPrice)). buy(Who,Stock,SellingPrice) :- bg(try_to_buy(Who,Stock,SellingPrice)).buy(Who,Stock,SellingPrice) :- bg(try_to_buy(Who,Stock,SellingPrice)). try_to_buy(Me,Stock,LimitPrice) :- wait_for(offer(You,Stock,YourPrice), less(YourPrice,LimitPrice)),……….try_to_buy(Me,Stock,LimitPrice) :- wait_for(offer(You,Stock,YourPrice), less(YourPrice,LimitPrice)),……….

19. mobile threads with First Order AND-continuations a:-b,c,d.a:-b,c,d. binarization: a(C)::-b(c(d(C))).binarization: a(C)::-b(c(d(C))). get_cont(C,C)::-true(C). % binarizedget_cont(C,C)::-true(C). % binarized mobile threads algorithm:mobile threads algorithm: –move/0: get continuation, send over the net, resume execution on target –return/0: send back new continuation, resume execution back home

20. A Simple API for mobile threads run_server/0, here/0, there/0, where/1: => local or remote focusrun_server/0, here/0, there/0, where/1: => local or remote focus set_this_host/1, set_this_port/1, set_that_host/1,set_that_port/1set_this_host/1, set_this_port/1, set_that_host/1,set_that_port/1 move/0, return/0 vs. remote_run/1move/0, return/0 vs. remote_run/1 the(Pattern,Goal,Answer): runs Goal to produce the(Answer) or no => here/there switch =>local/remotethe(Pattern,Goal,Answer): runs Goal to produce the(Answer) or no => here/there switch =>local/remote

21. Examples of mobile threads Window 1: a mobile threadWindow 1: a mobile thread ?- there, move, println(on_server), member(X,[1,2,3]), return, println(back).?- there, move, println(on_server), member(X,[1,2,3]), return, println(back). Window 2: a serverWindow 2: a server ?- run_server.?- run_server.

22. mobile threads vs. RPCs: move once, compute many times ?-for(I,1,1000), remote_run(println(I)), eq(I,1000).?-for(I,1,1000), remote_run(println(I)), eq(I,1000). ?-there, move, for(I,1,1000), println(I), eq(I,1000).?-there, move, for(I,1,1000), println(I), eq(I,1000).

23. mobile threads: WHY? Large database, small agentLarge database, small agent Speed-up: move to a fast processor and back, transparentlySpeed-up: move to a fast processor and back, transparently here/there switch: same code can be run locally or remotelyhere/there switch: same code can be run locally or remotely fault tolerance - move->run->come backfault tolerance - move->run->come back

24. Emulating multiple answer computations ?- there, move, findall(X,for(I,1,3),Xs), return, member(X,Xs).?- there, move, findall(X,for(I,1,3),Xs), return, member(X,Xs). X=1;X=1; X=2;X=2; X=3X=3

25. A Classic Design Pattern, Publish/Subscribe: with mobile threads + Linda operations a reactive channel listener: in/1 loopa reactive channel listener: in/1 loop ?-listen(fun(_)).?-listen(fun(_)). selective channel publisher: out/1 loopselective channel publisher: out/1 loop ?-talk(fun(jokes)).?-talk(fun(jokes)). will not match:will not match: ?-talk(stocks(quotes,nasdaq))?-talk(stocks(quotes,nasdaq))

26. The (Pseudo) Server in The Client Pattern a client thread emulates server functionality (works behind a firewall!):a client thread emulates server functionality (works behind a firewall!): –pseudo_sever:- in(todo(X)),call(X),pseudo_server. –?- there,pseudo_server. the real client: there,out(todo(…))the real client: there,out(todo(…)) communication: through a blackboard on a (shared) real servercommunication: through a blackboard on a (shared) real server

27. The Octopus Agent Pattern branch out multiple Reactive Agent threads to various places using RPCs or mobile threadsbranch out multiple Reactive Agent threads to various places using RPCs or mobile threads watch for patterns containing returned results on the local blackboardwatch for patterns containing returned results on the local blackboard failure or non-termination of one thread have minimal effect: fault tolerancefailure or non-termination of one thread have minimal effect: fault tolerance

28. Designing Jinni Agent Classes Java layer:Java layer: –code and type inheritance –reaction to events –libraries: Java3D, XML, special devices etc. Prolog layer Prolog layer –knowledge processing –reaction to blackboard constraints –mobile threads

29. Design Patterns in Visual Jinni drives Java objects through handlesdrives Java objects through handles meta-programming is essentialmeta-programming is essential Java events interoperate with blackboard coordinationJava events interoperate with blackboard coordination easy extension to incorporate access to Java2D and Java3d librarieseasy extension to incorporate access to Java2D and Java3d libraries

30. jdialog/2: a Jinni Visual Component –jdialog(Q,A):- new_frame('Jinni Dialog',2,1,F), set_layout(F,border), new_label(F,Q,_),new_panel(F,flow,P), new_button(P,'Ok',out(bchoice(P,ok)),_), new_button(P,'Cancel', out(bchoice(P,cancel))), show(F), in(bchoice(P,A)), remove_all(F), destroy(F).

31. Shared Virtual Worlds set-up a registration mechanism for agents on serverset-up a registration mechanism for agents on server publish state changes to shared blackboard on server with out/1publish state changes to shared blackboard on server with out/1 one thread for each “ghost” (remote client) - too expensive!one thread for each “ghost” (remote client) - too expensive! => shared update loop: get each ghost’s state with in/1 from server and update=> shared update loop: get each ghost’s state with in/1 from server and update

32. Shared Virtual Reality Agents Web based: browser+EAI+Java+JinniWeb based: browser+EAI+Java+Jinni Jinni Server + Thin Jinni Applet ConnectorJinni Server + Thin Jinni Applet Connector NEXT: Java 3D virtual worlds connected in a peer-to-peer Jinni networkNEXT: Java 3D virtual worlds connected in a peer-to-peer Jinni network new transport layers: Corba, RMI, HLAnew transport layers: Corba, RMI, HLA

33. Stock Market Agents reactive agents, user defined rulesreactive agents, user defined rules Internet data mining for stock quotesInternet data mining for stock quotes expressiveness far beyond the usual limit/stop/market transactionsexpressiveness far beyond the usual limit/stop/market transactions Octopus Agent Pattern: wait for triggers on multiple stocks, analyst info, indexesOctopus Agent Pattern: wait for triggers on multiple stocks, analyst info, indexes

34. Educational Agents chat, self organizing groupschat, self organizing groups alerts triggered by blackboard constraintsalerts triggered by blackboard constraints reusable agent hierarchiesreusable agent hierarchies student progress evaluationstudent progress evaluation Wizard of Oz help desk: combined human and programmed agentsWizard of Oz help desk: combined human and programmed agents

35. Mutual Agent/Host Security: the BRING YOUR OWN WINE PRINCIPLE reflective meta-interpreter: a few lines of Prolog - can be mobile - bring your own!reflective meta-interpreter: a few lines of Prolog - can be mobile - bring your own! can act as a sandbox protecting the Host against the Agent -statically checkedcan act as a sandbox protecting the Host against the Agent -statically checked undecidability of a Turing equivalent meta-interpreter protects the Agent against the Hostundecidability of a Turing equivalent meta-interpreter protects the Agent against the Host

36. Conclusion expressiveness of LP + a few simple, orthogonal concepts: engines, threads, blackboards, mobile threadsexpressiveness of LP + a few simple, orthogonal concepts: engines, threads, blackboards, mobile threads LP is ready for GUI-enabled, networked applications interoperating with mainstream software artifactsLP is ready for GUI-enabled, networked applications interoperating with mainstream software artifacts