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- 1. Study and development of methods and tools for testing, validation and verification of multi-agent systems University of Murcia Department of Information and Communication Engineering Author: Emilio Serrano Fernández Supervisors: Juan Antonio Botía Blaya José Manuel Cadenas Figueredo
- 2. Outline 1 2 3 4 5 2 Motivation Quantitative analysis for general MASs Qualitative analysis for MASs with protocols semantically annotated Analysis for MABSs used as model of AmI systems Conclusions and future lines
- 3. Outline 1 2 3 4 5 3 Motivation Quantitative analysis for general MASs Qualitative analysis for MASs with protocols semantically annotated Analysis for MABSs used as model of AmI systems Conclusions and future lines
- 4. ● Software is used for more life-critical applications every year. ● Bugs cost the U.S. economy an estimated 59.5$ billion annually. ● Bugs must be found by testing, verification, validation and debugging. ● The most common debugging strategy is by brute force. ● Multi-agent systems (MASs): kind of distributed system ● Interaction among agents is performed by complex dialogues. ● Debugging a MAS in which intelligent or emergent behaviors may appear is much more complex. ● This thesis deals with the analysis of interactions in large-scale MASs. Motivation 4 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions
- 5. ● Categorized in the forensic analysis field ● Providing a series of simple displays ● Defining and testing protocols automatically ● Discovering knowledge in the event log ● Shortcomings which motivate this thesis and goals ● Order of the logged events ● Abstract of the presented information ● Detection of undesired emergent behaviors ● Usability ● Analyzing semantics Three main MAS analysis approaches 5 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions
- 6. ● General large-scale FIPA MASs ● Very few elements are assumed. ● MASs with protocols semantically annotated. ● Annotations allows to study semantics. ● Multi-agent based simulations (MABS) which model Ambient Intelligence (AmI) applications. ● Why such a concrete system? Types of MAS studied 6 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions
- 7. Outline 1 2 3 4 5 7 Motivation Quantitative analysis for general MASs Qualitative analysis for MASs with protocols semantically annotated Analysis for MABSs used as model of AmI systems Conclusions and future lines
- 8. Abstract of the thesis 8 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions
- 9. ● General approach to provide forensic analysis of runs ● Capturing, ordering, and representation of messages ● Capturing: Aspect oriented programming (AOP) is employed. ● Relational data base (RDB) stores the messages. ● Transparent to developer ● Ordering: The global state is distributed and that a common time base does not exist ● Vector clocks: an array of n integers in a MAS with n agents ● m1_ v1 happened before m2_ v2 if v1 < v2 ● Management required → AOP Infrastructure for Forensic Analysis of Multi-Agent Systems 9 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions
- 10. ● They help to find typical bugs: ● Uninitialized agent, failure to send, wrong recipient, message sent multiple times, and wrong message sent. ● Sequence diagrams are very popular ● There is an information loss. ● The use of order graphs is proposed ● Concurrent events are clearly shown. ● Acyclic directed graph OG(V,E), where V are the selected messages and E the transitivity reduction of the less-strict relation between vector clocks. Studying messages via simple displays 10 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions
- 11. Order graph for case study, fire example 11 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions
- 12. ● When the number of messages and agents grows, the utility of the visual representation decreases. ● Abstract graphs and collaboration graphs Summarizing the order graphs 12 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions
- 13. ● How to detect undesired emergent behaviors? ● Database M with messages → Data mining ● Fields: sender, receivers, performative and content. ● Patterns can be detected ● Association rules and Clustering Intelligent Data Analysis over the Database for Forensic Analysis 13 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions
- 14. ● Association rules: technique to discover relations between variables in large databases ● Example: at1=v1 and at2=v2 then at3=v3 and at4=v4 (0.5) ● Relations between fields in the messages ● Goals: bugs detection and automatic testing ● MAS typically has a non-deterministic behavior Discovering relationships among attributes by association rules 14 PERFORMATIVE=CFP ==> SENDER=client conf:(1) PERFORMATIVE=ACCEPTPROPOSAL ==> SENDER=client conf:(1) PERFORMATIVE=REJECTPROPOSAL ==> SENDER=client conf:(1) PERFORMATIVE=PROPOSE ==> RECEIVER=client conf:(1) PERFORMATIVE=INFORM ==> RECEIVER=client conf:(1) ... Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions
- 15. ● Discovering groups of agents which behave similarly ● Similarity → agents which communicate with same agents ● ROCK algorithm is applied → categorical data ● M in the form of messages is not valid anymore ● Two agents are neighbors if they communicate with the same agent ● Clusters → agents which share a high number of neighbors. Discovering agents behaving similarly by the ROCK clustering algorithm 15 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions
- 16. ● Discovering agents which collaborate ● Collaboration → large interchange of messages ● The k-means algorithm is applied → vectors of real numbers ● Vectors must reflect the communication activity ● Groups are formed with agents which had a high communication activity among them Discovering agents interacting together by the k-means clustering algorithm 16 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions
- 17. Collapsed similarity and collaboration graphs for a case study, cinema example 17 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions
- 18. ● Clustering approach presents some limitations ● Intra and inter cluster visual information ● Information about the most important elements ● Amount of knowledge required by the user ● A solution is the use of techniques based on social networks analysis (SNA) ● Collaboration graph → social network ● Pathfinder algorithm generates pathfinder networks (PFNETs) ● They allow to reveal the underlying organization of a system ● P.e. psychology and scientometrics ● It keeps only those links whose weights do not violate the triangle inequality Analysis by Pathfinder Networks, Improving the Clustering Approach 18 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions
- 19. ● A PFNET(r, q) is a septuple (N, E, W, LLR, LMR, r, q) ● Only (A, E, W) is considered ● Similarity PFNET ● W function bounded by (0,1] ● 1 if agents interact with same agents using same number of messages ● Collaboration PFNET ● W function bounded by [0,1) ● 0 if agents do not interact at all Obtaining Pathfinder Networks for agents 19 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions
- 20. Collaboration PFNET for a case study, cinema example 20 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions
- 21. Implementation, ACLAnalyser 21 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions Presentation: videosACLAnalyser.mp4 Project website: aclanalyser.sourceforge.net/
- 22. ● Quantitative analysis for general large-scale MASs ● General infrastructure for forensic analysis ● AOP, RDB, vector clocks ● Simple displays for MASs runs. ● Order graphs, sequences diagrams, abstract graphs, collaboration graphs ● Data mining ● Apriori, ROCKS, K-means ● Social network analysis ● Pathfinder → PFNETs (similarity and collaboration) ● Implementation ● ACLAnalyser Contributions of this part 22 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions
- 23. Outline 1 2 3 4 5 23 Motivation Quantitative analysis for general MASs Qualitative analysis for MASs with protocols semantically annotated Analysis for MABSs used as model of AmI systems Conclusions and future lines
- 24. Abstract of the thesis 24 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions
- 25. ● Qualitative approach ● The target MAS cannot be general → Requirements to capture semantics ● Protocols specification ● Protocol results + semantics + data mining → context models ● Logical theories that capture regularities in previously observed interactions ● Utility? ● Summary ● Predictions, infer the mental states definitions, trustworthiness Qualitative Analysis from Interactions, Using Protocols Semantically Annotated 25 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions
- 26. Protocols Semantically Annotated, a negotiation 26 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions Protocol paths + ground terms = context = tuples for data mining
- 27. ● Data mining algorithms usually assume a fixed number of attributes ● Strategies for preprocessing ● Different paths → Different messages/constraints/variables ● Different data set for each observed path ● Using all messages/constraints and “?”· ● Iterations → Several constants g1,g2...gn ● N “copies” of each variable can be kept ● Considering only the first/last ground term g1/gn ● Class ● Protocol result/Constraint result Mining protocol executions: Dealing with paths, loops, and variables 27 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions
- 28. Results for a case study, car negotiation 28 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions
- 29. Implementation, Protocol miner 29 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions
- 30. ● Qualitative analysis for MASs with protocols semantically annotated ● Context models = data mining over paths and semantics ● Formal framework for protocols ● Strategies for the preprocess ● Experimental results ● Implementation Contributions of this part 30 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions
- 31. Outline 1 2 3 4 5 31 Motivation Quantitative analysis for general MASs Qualitative analysis for MASs with protocols semantically annotated Analysis for MABSs used as model of AmI systems Conclusions and future lines
- 32. Abstract of the thesis 32 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions
- 33. ● Multi-agent based simulations (MABS) ● Ideal technology for the validation of AmI applications. ● Ambient Intelligence (AmI) ● Applications are endless ● Smart homes, health monitoring and assistance, hospitals, transportation, emergency services, education, workplaces, etc. ● Direct validation may be impractical. ● Methodology for the development and analysis of MABSs to validate AmI applications. ● MABSs are complex → forensic analysis and new techniques. Forensic Analysis of MABSs as Tool to Develop AmI Systems 33 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions
- 34. 34 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions AmI Systems
- 35. AVA, An Agent based methodology for the Validation of AmI systems 35 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions
- 36. Forensic analysis infrastructure for MABS 36 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions
- 37. Results for a case study, UbikSim 37 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions
- 38. Implementation, UbikSim 38 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions First release: videosUbikSimHD.mp4 Current version: videosUbikSim-office.avi Project website: ubiksim.sourceforge.net/
- 39. ●AVA methodology ●Analysis of MABSs which model AmI systems ●Simpler simulations ●Reality injection ●Forensic analysis ●Implementation, UbikSim Contributions of this part 39 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions
- 40. Outline 1 2 3 4 5 40 Motivation Quantitative analysis for general MASs Qualitative analysis for MASs with protocols semantically annotated Analysis for MABSs used as model of AmI systems Conclusions and future lines
- 41. ● Contribution: advancing the state of the art in the analysis of multi-agent systems (MASs) interactions. ● Based on a forensic analysis. ● Very few elements that can be assumed for the analysis of a general MAS interactions. ● Database with messages. ● An infrastructure for forensic analysis has been provided. ● AOP, RDB, Vector clocks, Order graphs, Sequences diagrams, Collaboration graphs, and Abstract graphs. ● Interaction among agents can generate unwanted emergent behaviors. ● Learning techniques can be applied: association rules and clustering. Conclusions I 41 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions
- 42. ● Human intervention is required to assess the results of data mining ● Pathfinder networks (PFNETs) ● Clues about unwanted behaviors which must be confirmed ● Restrictions on the MAS design allows a more a powerful analysis ● Protocols semantically annotated allow to study not only the “envelope” ● Definition, creation, and analysis of Multi-agent based simulations (MABSs) used as model to validate Ambient Intelligence (AmI) systems ● Testing AmI complex and costly → MABS are also complex systems → forensic analysis methods and tools ● We have to keep working Conclusions II 42 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions
- 43. ● Quantitative analysis ● Studying new aspects in agent societies to be represented. ● Studying alternative techniques. ● Improving the degree of automation. ● Qualitative analysis ● Using more real-world examples. ● Studying the performance for different strategies. ● Employing more advanced machine learning methods. ● Analysis for MABS ● Automatic construction and verification of the environment model. ● Using learning agents. ● More cases study. Future lines 43 Motivation | Quantitative analysis | Qualitative analysis | Analysis for MABS | Conclusions
- 44. Journals • Validating Ambient Intelligence based Ubiquitous Computing Systems by Means of Artificial Societies. Emilio Serrano and Juan Botía. Information Sciences. In press (Impact Factor 2010, 2.833, Q1) • Debugging complex software systems by means of pathfinder networks. Emilio Serrano, Arnaud Quirin, Juan Botia and Oscar Cordón. Information Sciences 180 (5) (2010) 561-- 583. (Impact Factor 2009, 3.291, Q1) . • Ubik: a multi-agent based simulator for ubiquitous computing applications. Emilio Serrano, Juan A. Botia, and Jose M. Cadenas. Journal of Physical Agents, 3(2), 2009. Indexed in Scopus. • Intelligent Data Analysis applied to Debug Complex Software Systems. Emilio Serrano, Jorge J. Gomez-Sanz, Juan A. Botia, Juan Pavon. Neurocomputing, 72(13-15):2785 -- 2795, 2009. (Impact Factor 2009, 1.440, Q2). Relevant Publications 44
- 45. Conferences • Mining Qualitative Context Models from Multiagent Interactions. Emilio Serrano, Michael Rovatsos and Juan Botía. Extended Abstract in AAMAS2011. • Human behaviours simulation in ubiquitous computing environments. Teresa Garcia-Valverde, Francisco Campuzano, Emilio Serrano and Juan A. Botia. Workshop on Multi-Agent Systems and Simulation (MAS&S) at MALLOW10. • Social simulation for AmI systems engineering. Teresa Garcia-Valverde, Emilio Serrano and Juan A. Botia. International Conference on Hybrid Artificial Intelligence Systems (HAIS'10), 2010. • Incremental deployment of large-scale AmI system by means of social models. Juan A. Botia, Emilio Serrano, Teresa Garcia-Valverde and Antonio Gomez-Skarmeta. Proceedings of International Workshop on ``Simulation of Complex Social Systems'' (SiCoSSys 2009). • Artificial societies immersed in an Ambient Intelligence Environment. Teresa Garcia-Valverde, Emilio Serrano, Juan A. Botia, Antonio Gomez-Skarmeta and Jose M. Cadenas. Proceedings of The 1st Workshop on Social Simulation on International Joint Conferences on Artificial Intelligence, 2009. • Infrastructure for forensic analysis of multi-agent based simulations. Emilio Serrano, Juan A. Botía Blaya and Jose M. Cadenas. Seventh international Workshop on Programming Multi-Agent Systems. Promas 2009 • Construction and debugging of a multi-agent based simulation to study ambient intelligence applications. Emilio Serrano, Juan A. Botía Blaya, and Jose M. Cadenas. International Work-Conference on Artificial Neural Networks (IWANN2009). • Infrastructure for forensic analysis of multi-agent systems. Emilio Serrano and Juan Botia. In Programming Multi- Agent Systems: 6th International Workshop, PROMAS 2008. • Testing and debugging of MAS interactions with INGENIAS. Jorge J. Gómez-Sanz, Juan Botia, Emilio Serrano, and Juan Pavón. In Agent-Oriented Software Engineering IX: 9th International Workshop, AOSE 2008 Relevant Publications II 45
- 46. • Jorge J. Gómez-Sanz and Juan Pavón • Complutense University of Madrid • Oscar Cordón and Arnaud Quirin • European Centre for Soft Computing • Michael Rovatsos • University of Edinburgh • Teresa Garcia-Valverde, Francisco Campuzano, and Andrés Muñoz • My group • Acknowledgements: Thesis supported by the Spanish Ministry of Science and Innovation under the grant AP2007-04080 of the FPU program, and in the scope of the Research Projects CARONTE, DIA++ and through the Fundación Séneca within the Program “Generación del Conocimiento Científico de Excelencia” Collaborators and acknowledgements 46
- 47. University of Murcia Department of Information and Communication Engineering Thank you very much for your attention "Science is forever a search, never really a finding. It is a journey, never really an arrival" Karl Popper 47

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