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Configuration & Recommendation


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Alexander Felfernig, Seda Polat-Erdeniz, and Christoph Uran, Graz University of Technology

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Configuration & Recommendation

  2. 2. AGILE Configuration & Recommendation: Agenda 1. Workpackage Mapping 2. Architecture Mapping 3. Configuration & Recommendation: goals, achievements, why it matters … 4. Ongoing Work 2
  4. 4. Workpackage Mapping 1. Configuration Technologies (WP2 & WP8) - Ramp-Up Configuration Technologies - Configurators supporting optimization scenarios - Example pilot: air pollution monitoring (+ smarthome) 4 2. Recommendation Technologies (WP2 & WP3) - Recommendation of IoT workflows - Cloud service recommenders - Recommendation of search heuristics and diagnoses
  6. 6. Architecture Mapping 6 - Recommenders in the cloud: Apps, Workflows, Services - Configurator in the cloud: Ramp-up scenarios - Recommendation & configuration: gateway services
  7. 7. A Closer View 7
  8. 8. Configuration & Recommendation
  9. 9. Goals of Configuration & Recommendation Technologies 9 1. Configuration Technologies (WP2 & WP8) - Enable flexible configuration knowledge representation - Efficient reasoning for solving configuration problems - Diagnosis & repair support (for inconsistent situations) 2. Recommendation Technologies (WP2 & WP3) - Recommendation of IoT workflows - Recommendation support for cloud services, devices, apps - Recommendation of search heuristics and diagnoses (personalized configuration & diagnosis services)
  10. 10. Achievements 10 1. Configuration Technologies (WP2 & WP8) - Answer Set Programming approach for representing IoT-related configuration knowledge bases (KBs) [ConfWS‘16,‘17] - Prototype Apps for air pollution mon. and smarth. [ConfWS‘16, ‘17] - Cluster-based learning for heuristics [IEA/AIE‘16] 2. Recommendation Technologies (WP2 & WP3) - Hybrid recommendation of IoT workflows, cloud services, and apps (content-based & collaborative filtering based) [JIIS‘17] - Utility-based optimization [JIIS‘16] - Cluster-based learning for personalized search & diagnosis [IEA/AIE‘16 ,ConfWS‘17]
  11. 11. Cluster-based Learning for Search Heuristics [IEA/AIE‘16] variables constraints 11 … v1 v2 vn … c1 c2 cm domain defs constraints & requirements new configuration task … cluster 1 cluster 2 cluster 3 search heuristics: value ordering x domain ordering clusters* *learned(geneticalgorithm)fromprevious configurationsessions(clusteringbasedonk-means) recommen- dation configuration search solution (configuration)
  12. 12. Cluster-based Learning for Personalized Diagnosis [ConfWS‘17] 12 variables constraints … v1 v2 vn … c1 c2 cm domain defs constraints & requirements inconsistent configuration task … cluster 1 cluster 2 cluster 3 heuristics: different constraint orderings clusters* *learned(geneticalgorithm)fromprevious diagnosissessions(clusteringbasedonk-means) recommen- dation direct diagnosis search diagnosis/ reconfiguration
  13. 13. Increasing Prediction Quality 13
  14. 14. Prototype Ramp-Up Configurator 14 Smarthome Configurator: Ramp-up Configuration (configuration and diagnosis)
  15. 15. Why it Matters? 1. Ramp-up of IoT environments: need for configuration technologies omnipresent! (up to 40x lower development costs!) 2. Tackling „no solution can be found“ dilemma in interactive scenarios 3. Recommendation technologies crucial for tackling „mass confusion“ (e.g. Helpful for non-expert workflow developers) 4. Efficiency a crucial factor for applicability 15
  16. 16. Ongoing Work 1. Increasing search efficiency of ASP based configuration KBs in very complex domains (through „lazy conflict detection“) 2. Extending existing prototype configuration knowledge bases 3. Improving the prediction quality of AGILE recommenders by taking into account further datasources, MF algorithms 4. Knowledge compilation (for gateway) using, e.g., binary decision diagrams (BDDs) 16
  17. 17. Selected Publications 17 1. A. Felfernig, S. Polat Erdeniz, P. Azzoni, M. Jeran, A. Akcay, and C. Doukas. Towards Configuration Technologies for IoT Gateways, International Workshop on Configuration 2016 (ConfWS'16), pp. 73-76, Toulouse, France, 2016. 2. A. Felfernig, M. Atas, S. Polat-Erdeniz. Cluster Based Direct Diagnosis. International Workshop on Configuration (ConfWS'17), Paris, France, 2017. 3. A. Felfernig, A. Falkner, M. Atas, S. Polat-Erdeniz, C. Uran, and P. Azzoni. ASP-based Knowledge Representations or IoT Configuration Scenarios. International Workshop on Configuration (ConfWS'17), pp. 62-67, Paris, France, 2017. 4. S. Polat Erdeniz, A. Felfernig, M. Atas, TNT. Tran, M. Jeran, and M. Stettinger. Cluster- Specific Heuristics for Constraint Solving, 30th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, Arras, France, pp. 21-30, 2017. 5. R. Walter, A. Felfernig, and W. Küchlin, Constraint-Based and SAT-Based Diagnosis of Automotive Configuration Problems, Journal of Intelligent Information Systems (JIIS), 2016. 6. T. Ulz, M. Schwarz, A. Felfernig, S. Haas, S. Reiterer, and M. Stettinger, Human Computation for Constraint-based Recommenders, Journal of Intelligent Information Systems (JIIS), 2016. 7. J. Tiihonen and A. Felfernig. An Introduction to Personalization and Mass Customization, Journal of Intelligent Information Systems (JIIS), pp. 1-6, 2017.
  18. 18. THANK YOU
  19. 19. Appendix: Review AGENDA 19 9.00 Start 15min Brief intro (recap of previous episodes, main architecture / achievements in a nutshell) — Raffaele 9.15 (30min) IoT Hardware innovation: the Industrial and Maker's hardware gateway (roughly WP1) — Paolo (Ramon?) 9.45 (45min) Enabling rapid prototyping: AGILE gateway, device, protocol mgmt, software release, packaged components (roughly WP2+WP3) — Georgios + Csaba 10.30 (10min) Coffee Break 10.40 (20min) AGILE Development Environment, demo — Csaba (WP3) 11.00 (30min) AGILE Research, brief results overview: recommender and configuration + security — Alexander/Seda + Juan David (recommender and WP5) 11.30 (60min) IoT and Cloud services interactions (including demo) — Roman (WP4) 12.30 lunch (1.5hr - can be shortened in case of time constraints) 14.00 restart 14.00 (90min) AGILE Pilots (focus on use of AGILE architecture in pilots and on what innovation potential came out of those) — Andreas (+ Pilot leaders) (WP8) 15.30 (20min) innovations radar — Jonas 15.50 (10min) Coffee Break 16.00 (20min) impact, open calls and external collaboration — Johnny (WP6) 16.20 (20min) partnership and dissemination — Philippe (WP7) 16.40 (30min) Administrative / financial — Margherita (WP9) 17.10 (35min) reviewers / PO debriefing 17.45 (15min) PO to present main conclusions / first feedback 18.00 end (can be extended until 18.30 at the latest in case of overrunning the schedule)
  20. 20. Addon 20
  21. 21. ASP Configuration Knowledge Representations [ConfWS‘17] 1. Partof Relationships 2. Generalization Hierarchies 3. Type-level Constraints 4. Symmetry Breaking 5. Lazy conflict detection (for domain reduction) 6. Direct Diagnosis (Anytime) 7. Configuration & Reconfiguration 21