Group: Architecture, frameworks and tools to regulated open MAS Presented by Carolina Felicissimo

Carolina Howard Felicíssimo © LES/PUC-Rio Outline Motivation State of the Art Most Relevant Authors Open Problems An Architecture for Regulated Open MAS Conclusion Future Trends

Carolina Howard Felicíssimo © LES/PUC-Rio Motivation For an Open MAS, how to express the –Environment’s laws –Organizations’ laws –Roles’ laws –Interactions’ laws in a consistent and correct way, respecting inheritance? Many formalisms’ techniques from Artificial Intelligence but the Architecture for regulated open MAS will follow the principles of Software Engineering

Carolina Howard Felicíssimo © LES/PUC-Rio Motivation Examples: An Environment’s Law: –Everybody have to obey traffic laws An Organization’s Law: –Everybody have to stop the car in red traffic lights A Role’s Law: –Everybody is permitted to cross the red traffic lights after 10:00 PM, if the interactions’ laws are obeyed An Interaction’s Law: –No car driver can collide with other car drives –No car driver can collide with pedestrians

Carolina Howard Felicíssimo © LES/PUC-Rio Motivation Which is the best formalism for representing knowledge? It has to: –Provide a precise characterization of the knowledge base to be used –Find implicit consequences of its explicit represented knowledge –Exploit the notion of hierarchical structure –Be easy to represent –Provide reasoning efficiency The more expressive the language, the harder the reasoning !!! How to balance expressiveness and reasoning?

Carolina Howard Felicíssimo © LES/PUC-Rio State of the Art Deontic Logic –Founded by Georg Henrik von Wright in 1951 Description Logic –Semantic networks in 1967 –Frame systems in 1981 –Network based-structures in 1992 Defeasible Logic –Founded by Donald Nute in 1987 Defeasible Description Logic –Guido Governatori. Rules and Rule Markup Languages for the Semantic Web. LNCS 3323, pp , 2004

Carolina Howard Felicíssimo © LES/PUC-Rio State of the Art Deontic Logic Develop a technique for deciding, whether the propositions it studies are logically true or not -> Decision Problems can be solved -If an act is permitted, then it is not prohibited Ex.: Walk in sidewalks is permitted If an act is obligatory, then it is permitted and it is not prohibited Ex.: Drive in roads is obligatory If an act is prohibited, then it is not obligatory and it is not permitted Ex.: Walk in roads is prohibited If is not permitted not perform an act, then this act is obligatory Ex.: Not drive in roads is not permitted

Carolina Howard Felicíssimo © LES/PUC-Rio State of the Art Deontic Logic -If is prohibited not perform an act, then this act is obligatory Ex.: Not drive in roads is prohibited Ex.: Not stop in red traffic lights is prohibited -If is permitted or is obligatory not perform an act, then this act is prohibited Ex.: Drive in roads is permitted Ex.: Not drive in red traffic lights is obligatory -If is permitted perform and not perform an act, then this act is permitted Ex.: In a smoking compartment is permit to smoke or not. So, is permitted to smoke

Carolina Howard Felicíssimo © LES/PUC-Rio State of the Art Description Logic –Monotonic formalisms based upon first-order logic –First order logic is inappropriate for reasoning on partial, incomplete and inconsistent knowledge base, and when there have conflicts -> can give wrong answers –Allows for the representation of concept conjunctions (Father Man Π Parent) concept disjunctions (Person Man Woman) concept negations (Woman ¬ Man Π Person) value or role restriction constructs ( hasChild.female)

Carolina Howard Felicíssimo © LES/PUC-Rio State of the Art Defeasible Logic Defeasible: Adj. Capable of being annulled or invalidated –It is a simple but efficient formalism for nonmonotonic reasoning based on rules and priorities –Priorities on rules may be used to resolve some conflicts among rules –Rules may support conflicting conclusions –Skeptical: Conflicting rules do not fire Consistency is preserved –Classical negation is used in the heads and bodies of rules Negation-as-failure is not used but can be emulated –It doesn’t have disjunction –Low computational complexity

Carolina Howard Felicíssimo © LES/PUC-Rio State of the Art Nonmonotonic Reasoning Grigoris Antoniou Grigoris Antoniou “Provides formal methods that enable intelligent systems to operate adequately when faced with incomplete or changing information.” Can derive some meaningful solutions in the presence of conflicts in the knowledge base used Can deal with rules with exceptions

Carolina Howard Felicíssimo © LES/PUC-Rio State of the Art The Defeasible theory –Facts: are indisputable statements. Ex.: emu(tweety) “Tweety is an emu” –Strict rules: are rules, whenever the premises are indisputable (e.g., facts) then so is the conclusion. Ex.: emu(X) -> bird(X) “Emus are birds” –Defeasible rules: are rules that can be defeated by contrary evidence Ex.: bird(X) => flies(X) “Birds typically fly” –Defeaters: are rules that cannot be used to draw any conclusions. Their only use is to prevent some conclusions. They are used to defeat some defeasible rules by producing evidence to the contrary. Ex.: heavy(X) ~> ¬flies(X) “If an animal is heavy then it might not be able to fly” –A superiority relation among rules: it is used to define priorities among rules, i.e., where one rule may override the conclusion of another rule

Carolina Howard Felicíssimo © LES/PUC-Rio State of the Art Defeasible Logic –It has been applied to Legal Knowledge Legal Reasoning Automated Negotiation Contracts Business Rules Multi-agent Systems Normative Reasoning Defeasible Logic reasoning –It is a rule-based approach for efficient reasoning with incomplete and inconsistent information –Is not as strong or conclusive as the reasoning in Description Logic

Carolina Howard Felicíssimo © LES/PUC-Rio Defeasible Logic – An Example User Requirements & Preferences: –Carlos is looking for an apartment of at least 45m 2 with at least 2 bedrooms. If it is on the 3rd floor or higher, the house must have an elevator. Also, pet animals must be allowed. –Carlos is willing to pay $300 for a centrally located 45m 2 apartment, and $250 for a similar flat in the suburbs. In addition, he is willing to pay an extra $5 per m 2 for a larger apartment, and $2 per m 2 for a garden. –He is unable to pay more than $400 in total. If given the choice, he would go for the cheapest option. His 2nd priority is the presence of a garden; lowest priority is additional space.

Carolina Howard Felicíssimo © LES/PUC-Rio Defeasible Logic – An Example Predicates Used in Formalization: –size(x,y), where y is the size of apartment x (in m 2 ) –bedrooms(x,y), where apartment x has y bedrooms –price(x,y), where y is the price for x –floor(x,y), where apartment x is on the y-th floor –gardenSize(x,y), where apartment x has a garden of size y –lift(x), meaning that there is an elevator in the house of x –pets(x), meaning that pets are allowed in x –central(x), meaning that x is centrally located –acceptable(x), meaning that flat x satisfies Carlos’s requirements –offer(x,y), meaning that Carlos is willing to pay $ y for flat x

Carolina Howard Felicíssimo © LES/PUC-Rio Defeasible Logic – An Example Formalization of Requirements: –r1: => acceptable(X) –r2: bedrooms(X,Y), Y ¬acceptable(X) –r3: size(X,Y), Y ¬acceptable(X) –r4: ¬pets(X) => ¬acceptable(X) –r5: floor(X,Y), Y > 2, ¬lift(X) => ¬acceptable(X) –r6: price(X,Y), Y > 400 => ¬acceptable(X) –r2 > r1, r3 > r1, r4 > r1, r5 > r1, r6 > r1 –r7: size(X,Y), Y ≥ 45, garden(X,Z), central(X) => offer(X, Z + 5(Y−45))

Carolina Howard Felicíssimo © LES/PUC-Rio Defeasible Logic – An Example Formalization of Requirements: –r8: size(X,Y), Y ≥ 45, garden(X,Z),¬central(X) => offer(X, Z + 5(Y−45)) –r9: offer(X,Y), price(X,Z), Y ¬acceptable(X) –r9 > r1 –r10: acceptable(X), price(X,Z), not(acceptable(Y), Y ≠ X, price(Y,W), W cheapest(X) –r11: cheapest(X), gardenSize(X,Z), not(cheapest(Y), Y ≠ X, gardenSize(Y,W), W largestGarden(X) –r12: largestGarden(X), size(X,Z), not(largestGarden(Y), Y ≠ X, size(Y,W), W rent(X)

Carolina Howard Felicíssimo © LES/PUC-Rio Defeasible Logic – An Example AppBedSizeCentFloorLiftPetsGardPrice a1150yes1noyes0300 a2245yes0noyes0335 a3265no2 yes0350 a4255no1yesno15330 a5355yes0noyes15350 a6260yes3no 0370 a7365yes1noyes12375

Carolina Howard Felicíssimo © LES/PUC-Rio Defeasible Logic – An Example Results of User Preferences: –Apartment a1 is not acceptable because it has one bedroom only (rule r2). –Apartments a4 and a6 are unacceptable because pets are not allowed (rule r4). –Apartment a2 is unacceptable because it costs more than the $300 Carlos is willing to pay (rules r7 & r9). –The rest, a3, a5 and a7, are acceptable. –Apartments a3 and a5 are the cheapest acceptable apartments (rule r10) –a5 is selected because it has larger garden than a3 (rules r11 and r12)

Carolina Howard Felicíssimo © LES/PUC-Rio State of the Art Defeasible Description Logic –Defeasible assertions are added to description logic –Reasoning on partial or incomplete knowledge bases, and when there exist conflicts in a decidable way –Should be used when an open world is assumed

Carolina Howard Felicíssimo © LES/PUC-Rio Most Relevant Authors Deontic Logic –Georg Henrik von Wright (Founder. 1951) Description Logic –Franz Baader, Diego Calvanes, Deborah McGuinnes, Daniele Nardi, and Peter Patel-Schneider Defeasible Logic –Donald Nute (Founder. 1987) Defeasible Description Logic –Guido Governatori (from University of Queensland, Australia) –Grigoris Antoniou (from University of Crete, Greece ) Normative Multiagent Systems –Guido Boella (from Università di Torino, Italy) –Leendert van der Torre (from CWI Amsterdam and TU Delft) Ontologies –Deborah McGuinnes, Ian Horrocks, Richard Benjamins, Peter Patel-Schneider

Carolina Howard Felicíssimo © LES/PUC-Rio Open Problems How to balance expressiveness and reasoning? Which is the best formalization to be used? –It depends on the problem that is being studied

Carolina Howard Felicíssimo © LES/PUC-Rio The Architecture First we have to decide what kind of formalism we will use Then, we can think about an architecture for regulated open MAS

Carolina Howard Felicíssimo © LES/PUC-Rio Conclusion Thousand of different logics !!! Each one for specific purposes :/ You have to find the best one that formalize your problem

Carolina Howard Felicíssimo © LES/PUC-Rio Future Trends Choose one formalism Balance the trade-offs about the choice made Structure the paper for the AAMAS-2006 Conference

Carolina Howard Felicíssimo © LES/PUC-Rio Bibliography Deontic Logic in Computer Science: Normative System Specification (Hardcover). Meyer and Wieringa. 400 pages. February, –Available at Amazon for 176 dollars :/ Defeasible logic. Donald Nute. In Handbook of Logic in Artificial Intelligence and Logic Programming, volume 3, pages 353–395. Oxford University Press, pages. –Available at Oxford University Press for 250 dollars :// Nonmonotonic Reasoning. Grigoris Antoniou. 275 pages. April, –Available at MIT Press for 55 dollars The Description Logics Handbook. F. Baader, D. Calvanes, D. McGuinnes, D. Nardi and P. Patel-Schneider, editors. Cambridge University Press, Cambridge, pages. January, –Available at The Cambridge University Press for 85 euros :/ –Or electronic with me without the Bibliography part

Carolina Howard Felicíssimo © LES/PUC-Rio Bibliography Law and the Semantic Web. Legal Ontologies, Methodologies, Legal Information Retrieval, and Applications. Lecture Notes in Computer Science from Springer. Volume 3369/2005 –Available electronic at &volume=3369&issue=preprint &volume=3369&issue=preprint Deontic logic, agency and normative systems. [Delta] EON'96, Third International Workshop on Deontic Logic in Computer Science / Mark A. Brown and José Carmo (eds.). International Workshop on Deontic Logic in Computer Science ( Sesimbra, Portugal) –Available at PUC-Rio’s library :)

Carolina Howard Felicíssimo © LES/PUC-Rio Links The University of Queensland, Australia ( Leendert van der Torre Publications ( Guido Boella Publications (