The document summarizes a presentation on configuration and recommendation technologies from AGILE. It discusses: 1. The goals of developing configuration technologies to enable flexible knowledge representation and efficient reasoning for solving configuration problems, and recommendation technologies to recommend IoT workflows, cloud services, and apps. 2. Key achievements including developing answer set programming approaches for configuration knowledge bases and prototypes for air pollution monitoring and smart homes, as well as hybrid recommendation systems and learning cluster-based search heuristics. 3. Ongoing work including increasing efficiency of answer set programming, extending knowledge bases, improving recommender prediction, and knowledge compilation for gateways.

1. AGILE M18 Review, 20 October 2017, Brussels (Belgium)
Configuration & Recommendation
ALEXANDER FELFERNIG, SEDA POLAT-ERDENIZ, AND
CHRISTOPH URAN, GRAZ UNIVERSITY OF TECHNOLOGY
(AGILE RESEARCH PARTNER)
1

2. AGILE Configuration &
Recommendation: Agenda
1. Workpackage Mapping
2. Architecture Mapping
3. Configuration & Recommendation: goals,
achievements, why it matters …
4. Ongoing Work
2

3. WORK-PACKAGE MAPPING

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

5. ARCHITECTURE MAPPING

6. Architecture Mapping
6
- Recommenders in the cloud: Apps, Workflows, Services
- Configurator in the cloud: Ramp-up scenarios
- Recommendation & configuration: gateway services

7. A Closer View
7

8. Configuration & Recommendation

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. 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. 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. 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. Increasing Prediction Quality
13

14. Prototype Ramp-Up Configurator
14
Smarthome Configurator:
Ramp-up Configuration
(configuration and diagnosis)

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. 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. 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. THANK YOU

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. Addon
20

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