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Agents and Semanticsfor Future Internet Applications
PD. Dr. Matthias KluschGerman Research Center for Artificial Intelligence
Saarbrücken, Germany
30th ACM Symposium on Applied Computing16.4.2015, Salamanca, Spain
Agenda
Future Internet
Perspectives
Agents and Semantic Technologies
Intelligent Applications
Showcases in Manufacturing, Retail,
Private and Social Life
Selected Challenges
2
Internet of Today
Number of users grows2015: 3 billion (1 billion 2005), 50 billion connected devices
Internet economy grows2016: 5% of GDP in G-20 countries
Variety and number ofapplications grows2017: 268 billion mobile apps
Internet usage growsPer minute: 200M emails, 100K tweets, 2M+ search queries, 3K photo uploads, …
Number of cyber attacks grows91% increase in 2013 (62% successful)
3
Future Internet Perspectives …
Internet of ThingsIP-connected, resource-aware, autonomous,self-coordinating smart things (aka smart objects)
Internet of ServicesEverything as a (Web) Service.IP-accessible, interoperable, reusable assets.Coordination in SOAs, in clouds (I/P/SaaS).
Internet of InteractionSocial networks and media sharing.Multimodal, virtual 3D & AR-based.
… each with its own• technological resources, standards
• institutions, research agenda
2017: 10B mobiles, 2050: 50B IP-connected things
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… Including a Quantum Internet
Today deployed quantum networks … in USA … in China
Based on quantum computing „Quantum big data“ processing
beyond boundaries of classical computational power
Based on quantum cryptography and networking Perfectly private communication (wired and wireless)
2003: DARPA-fundedQI backbone in USA(Boston, BBN, Harvard)
2014: USA nationwide(incl. Google, IBM, Microsoft,Quantum Data Centers/Labs)
2014 - 2016: China QI backbone(Beijing - Shanghai)
~ 2030: planned
Extension toworldwide QI
2000 km
Seth Lloyd et al. (2004). Infrastructure for the Quantum Internet. ACM SIGCOMM Computer Communications Review Volume 34(5)5
NSF (USA)
European Commission
Korea
Future Internet Convergence
• Towards what, how, when?
Nobody knows yet …• Several FI initiatives with different
research focus and proposals– FI architecture & infrastructure,
abstractions (SDN)– FI testbeds (FIRE), technologies,
generic enablers (FIWare) for networking, security, etc.
Expectationson FI applications
6
Future Internet Applications: The A* Vision
Smart TransportationSmart Cities
Smart Travel Smart
Home
Smart ManufacturingSmart Retail
Smart Energy
Real-time access to, andassisted coordination
• T Kelly, ITU Report 2005: The A4 vision: Anywhere, anytime, by anyone and anything.• J Hafkesbrink & M Schroll, TII conference 2010: A5 - Anything, Anytime, Anywhere, Anyway, Anyone. • L Castaneda et al. 2012: The Future of Internet Applications. Verizon 2015: State of the Market report. FIRA, FIWARE websites.
Smart Health
Anything, Anywhere, Anytime, on Any device, by Anyone, Intelligent, Safe & Secure
7
Intelligent FI Applications: Agents
From an AI perspective: Intelligent FI applications exhibitautonomous, rational, proactive, adaptive behaviorfor • Problem-solving and decision support
• Data, process and service coordination• Human-agent interaction
Intelligent agent technologies
• Agent modeling, execution platforms(e.g. Bochica, JACK, JADEX)
• Individual or joint AI planning, learning
• Inter-agent communication(e.g. FIPA-ACL, JADE)
• Inter-agent coordination(e.g. eCNP, Negotiation mechanisms, Swarm rules)
software, or robotic, or animalagent
8
Examples: Intelligent Agent Applications
Multiagent system for decentralized steel production lines control and optimisation.
• Multiagent systems for optimal fleet management, logistics.
• Trading Agents for negotiation support on B2B markets,
product recommendation, service brokerage.
Multiagent systemsfor soccer games
Semi-autonomous, cooperativeplanetary surface exploration
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Intelligent FI Applications: Semantics
• Semantic interoperation and analysisof data, streams, services, processes, actions
• Semantic explanation to human user
Semantic technologies
• Semantic modelling, management, reusewith formal ontologies or linked data cloud sets(e.g. W3C OWL2, W3C SSN, OWLIM, SAWSDL, OWL-S)
• Semantic search, analysis, mediation and compositionof linked data, data streams, processes and services(e.g. Hermit, Pellet, FSPARQL, C-SPARQL, Ztreamy, iSeM, OWLS-Xplan)
lod-cloud.net
10
Sorry …No general selection and usage strategy
for agents and semantic technologiesin intelligent Internet applications
… but lots of individual illustrating showcases
in different application areasIEEE IC March/April 2015
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SHOWCASES IN MANUFACTURING AND RETAIL
Agents and Semantics for Intelligent Internet Applications
12
Mechanical Electrical power Electronics IoT+IoS+IoI
Related Vision: Industry 4.0
Vertical and horizontal online integration of all IP-connected data and services
for situated optimal adaptation and execution of manufacturing processes
at runtime, in mixed reality.
Industry 4.0 apps
are IoT+IoS+IoI apps
with (some) A* properties.
In what manufacturing areascan agents and semantics help
13
Area: Condition-Based Machine Maintenance
Condition monitoring systems (CMS)
• Support detection of onset of faults
• Quantitative (statistical) analysis of data streams from IP-networked machine sensors
Results interpretable only by human expertsfor diagnosis and maintenance decisions
Semantic explanation of machine conditions and faults for human experts and non-experts (anyone)
Fast combined quantitative and semantic data analysisonline (anywhere, anytime)
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Showcase: iCM-Hydraulic App
Mobile iCMH web client
PLC
iCMH-System
MachineSensor
Network
Combines statistical, semantic and probabilistic analysis for fault detection and condition diagnosis with human understandable explanations.
Implementation: Java; OWLIM store; C-SPARQL stream analysis engine; reasoners STAR, Hermit; BN tool GeNIe, (MATLAB)
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iCMH Domain Ontology
Fact base: RDF encoded state and history data• Component / sensor instances• sensor measurements• detected conditions, faults, symptoms • fault probability values
Concept base:OWL2-DL encoded semantics of
• Machine components, sensors,measured properties
• Component faults, symptoms,condition, operational factors
• Condition-fault-symptom relations
(279 concepts, 184 relations)Modelling
• Expert interviews at HYDAC
• CM standards ISO 2041, 13372,
17359:2011
• W3C Semantic Sensor Network Ontology
(part of concept base)
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iCMH Belief Network
Pump Leakage Fault F P(F|C)
Pump Leakage Symptoms S e.g. Pressure Level After Load P(S|F)
Pump Condition C P(C|EF)
External Factors P(EF):e.g. PLC Signal, Operational Pump State
Probabilistic relations:
(36 variable nodes, 60 relations)Dynamic update after eachsymptom, fault detection.
Fault detectionF: Max P(F|S)
Condition diagnosis{S}: P(C|S) >
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P( Pump_Leakage = Onset | SPAL = low, POS = active ) = 0.7Probabilistic analysisMax. probable fault state:
EvidencesEvidences
Semantic annotation
Semantic feature streamanalysis• Logical inference of symptoms
(C-SPARQL rules) and diagnosis (Hermit, STAR reasoner)[ Pump_Leakage = Onset, Valve_Op_Degradation = No,Cooling_Op_Degradation = No,Accumulator_Gas_Leakage = No ]
Feature extraction50K 90
Symptom: (Static_Pressure_After_Load hasState “low”), Factor: (Pump_Op_State hasState “active”)
Statistical feature streamanalysis• Trained fault state classifier (LDA)
e.g.
Note: Wrong statistical fault state but correct semantic symptom detection is recognized by BN.
Hybrid Fault Detection Online
Multi-variate sensor data stream
(from PLC for 20 sensors) [ ][ TS = “22.10.2014T22:10:23”; cool_temp = (.., 21.0, 34.0,..); valve_pressure = (11.0, 1.0,..); … ]
1 min machine work cycle
326 KB
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Semantic Explanation
1. Most likely explanation of machine [component] condition?19
Semantic Explanation (2)
Semantic diagnosis online
(in parallel over stream)
2. Semantic relation between
detected [component] faults?
3. Other components affected
by detected [component] fault?
PLUS: Semantic diagnosis offline.
Query-specific pattern-basedgeneration of explanation.
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Example
Semantic relation between component faults detected at same time ?
1. Find shortest path in ontology between
faults (ipl_234, agl_456) with STAR reasoner
2. Rule-based interpretation of object relations
in this path, e.g. „object location“ rule:
(?x connectedTo ?a, ?a ... ?b, ?b connectedTo ?y)
(?x before ?y)
3. Aggregation of results into an explanation text pattern for this query type:
Pump pump_123 with internal pump leakage ipl_234 is located before faulty componentaccumulator accu_3457 with gas leakage agl_456, detected at time 12.03.2015 23:00:09.Therefore, detected internal pump leakage might have caused detection of accumulatorgas leakage.
internal pump leakageevent ipl_234
accumulator gas leakageevent agl_456
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Online Analysis Performance
2015
• Fast average response• Semantic stream: 0.3 sec
• Hybrid fault detection: 25 sec
• Statistic classification: 0.5 sec
• Fault relations: 0.6 sec
• Affected components: 40 sec
• BN exact update: 1 sec
• App-specific fixed throughput • 600 RDF triples/min or 2K triples/min (w/ or w/o feature reduction)
• Scales up to > 80k triples/sec (C-SPARQL SparkWave w/reasoning)
SparkWave
C-SPARQL
? in cloud with STORM-based SPARQLstream[UP Madrid 2014]
22
Next: Online Assisted Maintenance
Extreme case: Self-maintaining „smart objects“ e.g. smart product with embedded semantic memory
(in XML-OMM) of its state and handling across lifecycle
iCM Agent automatically
• Generates maintenance plan based on its
hybrid semantic diagnosis online
– Semantic text retrieval (digital handbook)
– Semantic matching of similar cases (history data)– Semantic state-based planning (world state)
• Informs and instructs mobile worker in mixed reality
… tells machines about its state and how to grasp it (lifting points) –robot decides with which service to handle it in its current state.
av.dfki.de
DFKI FemBot„AILA“
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Area: Virtual Factory Simulation & Training
Intelligent virtual human-machine interaction (anyone) with
integrated semantic explanation and verification (safe)
in a 3D web space (anytime, anywhere)
Virtual factory simulates manufacturing processes
of real-world factory in 3D environment for
• Testing of optimal functionality, safety,
ergonomics issues of assembly-lines
• Training of workers on (new) machines
Ford, BMW, Volvo, Yamaha, …
24
Showcase: ISReal-SmartFactory App
Virtual factory model in annotated XML3D scene
AnnotatedXML3D scene Verification HAVLE
(Hybrid Automata Verification by Location Elimination)
Semantic planningOWLS-Xplan2
Semantic reasoningDL-based, object relational
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Example
Semantic planning of machine services and
handling actions for given goal
Time verification of created action plan
Explanation (text2speech, simulated plan execution)
User query-answering on functionality of machine:
Can I produce 20 pills
with this machine
within 30 secs ?
Yes, you can. I show you how to handle the machine for that ….26
Example (2)
Time-based verification of plan execution
3D visualization of failure trace + T2S (explanation)
Building of alternative plan with revised state
Why did my handling of
this machine fail ?
Sorry, you lifted the carriage stopper eight seconds too late (max 2.5s) !
Here is your correct control plan for handling this machine…
• Explanation of detected failures of machines, or their handling by user
• Show alternatives to user
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Area: Collaborative Virtual Factory Engineering
Collaborative virtual design
and simulation of factory
by multiple IP-connected engineers
online.
• Real-time synchronized multiple interactions and simulations
• Support of high-precision, fast joint search for 3D models
• in shared 3D web space
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Showcase: C3D-SmartFactory App
Near-real time synchronized view
• 3D factory model and design actions by multiple engineers• multiple avatars (BDI agents) in scene
in 3D scene of shared XML3D web space
XML3D & FiVeSmiddleware web client
I. Zinnikus et al. (2013): 9th IEEE Intern. Conf. on Collaborative Computing (CollaborateCom)29
Semantic 3D Object Search
Local search with semantic 3D index iRep3D
• Hybrid semantic matchingof annotated XML3D, X3D, COLLADA objects(geometry, text, concept, semantic services)
Distributed P2P search with S2P2P
• Semantic expert-driven query routing
- Local learning of semantic overlay- Joint routing path generation with maximum
#expert peers for query topic within TTL
• Dynamic semantic 3D object replication
• Local alignment of individual annotationontologies to query semantics
X. Cao, M. Klusch (2013): S2P2P. 15th IEEE Intern. Conf. on High-Performance Computing and Communication HPCC. iRep3D. 8th Intern. Conf. on Computer Vision Theory and Applications.
High-precision, robust, fast
• P2P (1M, 3DS-TC): 0.8 AP in 3s • Local:
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Simulation & Verification
Formal verification of safety properties of designed factory components (with HAVLE)
3D visualisation of failure traces to human user
Simulation of human user behaviorswith configurable BDI-agent types
31
Showcase: C3D-Retail App
• Collaborative product placement in virtual supermarket by
• Layout designers • Test customers via instrumented
supermarket (Dual reality)
• Agents simulate types of customerbehavior in virtual supermarket
• Optimization of supermarket layout[customer preferences / sales revenue]
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Showcase: ADIGE App
Product alert: selection, execution
Instrumented supermarket (RFID, EBS)
Productre-orderingprocesses
Virtual supermarket in dual reality management dashboard
2014
Process adaptation to dynamically changing process services(availability, new, SLA) at runtime.
Reactive semantic re-/planning of servicesof annotated process models in OWL2.
DemoVid
33
Challenge: Cloud Manufacturing
Large-scale
• Semantic integration, analysis, search, composition,
negotiation of manufacuring process data & services
in the cloud
large-scale semantic data analysis in the cloud (WebPIE, etc.)
Xu, X. (2012): From cloud computing to cloud manufacturing. Robotics and Computer-Integrated Manufacturing, 28
• Elastic process services execution (wrt. resources, quality, costs)
• Resource-aware semantic services coordination (mediation, planning)
• Process optimisation online (semantic stream reasoning/CEP + reactive service composition + Business analytics)
crema-project.eu (2015 – 2017)
34
Challenge: Human-Agent Teamwork
• Semantic modelling, reasoning and interaction
mutual understanding of behaviors, functional capabilities, incentives, intentions, goals across all team members – fast … very fast …
• Integrated hybrid team planning, negotiation, execution, aggregation
@DFKI (ongoing)orchid.ac.uk (ongoing)
Agent types
1. Software
2. Robots
3. Animals
4. Animoids
interactive.mit.edu
Advanced Manufacturing
charm.sites.olt.ubc.ca (ongoing)
Desaster Rescue
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Challenge: Quantum Internet of Services
Quantum computing and information processing
assets of QI-connected quantum computers
are provided as quantum services Search, Scientific Simulations (Weather, Life
Sciences, Finance, Defences, Energy),
De-/Encryption, etc.
M Klusch (2008): Toward Quantum Matchmaker Agents. ACM/IEEE Intern. IAT Conference
M Klusch (2004): Toward Quantum Computational Agents. LNAI 2969, Springer.
How to describe their semantics
How to coordinate (select, compose) them for QI apps
with agents on hybrid quantum computers
Source: market research media512qbit (2012), 4.2 PFLOPs, 10M$
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SHOWCASES INPRIVATE AND SOCIAL LIFE
Agents and Semantics for Intelligent Internet Applications
37
Area:Intelligent Recommendation
Exploit• Semantic model of domain, user profile,
sensed external data (streams)• Semantic item relevance computation
– Knowledge graph analytical heuristics, DL-reasoning, social network data analysis, etc.
R Yan et al. (2013): Using semantic technology to improve recommender systems based on Slope One. JW Ha et al. (2014): EPE – An embedded personalization engine for mobile users. IEEE Internet Computing, 18(1) J Pazos Arias et al. (2012): Recommender systems for the social Web. R De Virgilio et al. (2012): Semantic search over the Web.
NewsTV channels
MoviesVideos
Music
Improved accuracy of item recommendations compared to non-semantic approaches
Semantic explanation of recommendations− Most relevant properties of item, or N-item property paths in knowledge graph, etc.
Mitigation of cold start problem
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Showcase: SPrank
(1) Computes heuristic path-based item relevancein profile-extended semantic LOD knowledge graph Frequency of different user-item path types (features) j
Semantic relevance ofitem i1 for user u3 (for |p| < 5):
• Collaborative path types#path(1) = (likes, likes, likes): 2
• Content-based path types#path(2) = (likes, p2, p1): 2#path(3) = (likes, p2, p3, p1): 1
• Hybrid path types: |p|>=5#path(4) = (likes,p2,p1,likes,likes)
x31 = (2/5, 2/5, 1/5)
likes i1
i2
u3
i3u2
u1
u4
u: user, i: iteme: entity (new user/item)p: property
e2e4
e1p1
p1p2 p2p2p1
p2
e5
p1 p2
p3
p2
p4
i4
User-item profile
?
p3
e3
Knowledge graph
T. Di Noia et al. (2013): ACM 7th Conf. on Recommender Systems (RecSys)
(2) Simple regression-based learning of rank f(xui) yieldshigher accuracy than with common standards!
(up to 0.6 recall >> BPRLin, SLIM, SMRMF with test data from MovieLens, Last.fm)
D
Ddui
uiui R
dpathjpathjx ∈=
∑∈
)(#)(#)(
39
Example: Semantic Explanation
For N-Item relational queries:
Display the shortest item-item paths
in the knowledge graph ….
(Corr. NP-hard Steiner tree problem solution)
…. and learn to improve accuracy based on implicit/explicit user feedback:
horst.dfki.de
40
Showcase: SmartCar-Agent
M Moniri, M Feld, C Müller (2012): Proc. 8th IEEE Conf. on Intelligent Environments.
That looks nice, is it worth a visit?
Human context-sensitive POI recommendation
− Object recognition and cognitive activity level of driver (eye tracking and cognitive load analysis system, semantic image matching)
− Semantic CB recommendation and multimodal presentationbased on actual driver profile and activity level to reduce distraction
Driver profileLocal knowledge graphSemantic image index
Yes, this baroque church, called Ludwigskirche, can be of interest to you + [Info, Nav]
42
Showcases: Smart Shopping and Cooking Assistance
Instrumented shopping cart with agent
• Searches in huge RDF knowledge graph, text databases, etc.
• Recommends recipes which combination of ingredients have
properties „tasty“, „combinable with each other“,
match user preferences and with novelty bias.
Shopping cart agent
− Reasons on sensed semantic memory of productsin shopping cart and surrounding
− Validates and recommends alternatives wrt. uploaded user profile (e.g. lactose intolerance),
product state (e.g. was opened before, crushed)
43
Area: Smart City and Social Events
Real-time tracking of crowds Identification of newly attractive,
and underserved areas,
Explanation of attraction shifts
Real-time weather forecasts
Real-time traffic monitoring Traffic prediction and diagnosis
Social Listening Milano
Semantic analysis and explanation• Streamed social network, sensor data• Non-streamed public (linked) data sources
may require computational complex combinationof stream querying / CEP, semantic IR, DL-based reasoning, etc.
Olympic Games 2012 London
44
Trading Off Real-Time for Accuracy ?
STAR-City traffic diagnosis system (deployed in Dublin, Bologna, Miami, Rio)
combines fast stream querying with DL-based and probabilistic reasoningto diagnose traffic congestions given sensed actual and history data in cloud.
Diagnosis response times worse than in current TMS but way more accurate
2.7hrs
F Lecue (2014): Semantic Traffic Diagnosis with STAR-CITY. Intern. Semantic Web Conference (ISWC)
Experiments with 6 OWL/LOD ontologies (~140 KB), online & offline data (~70 GBs/day) on vehicleactivity, traffic stats, incidents, road works, social network analysis, social media events planned, etc.
45
Alternative: Brute Force in Cloud
experiments with (petabyte scale) traffic data from highways in Bavaria achieved an avg.
95% prediction accurracy of traffic congestions within 30 minutes
Experiments with using lower expressive semantics e.g. in OWL2-RL
for reasoning revealed that accurate prediction of traffic congestion
events can be achieved in near-real time (within 5 secs).
A Pascale et al. (2015): 21st Sympos. Transporation and Traffic Theory.B Gorman et al. (2014): Traffic Management using RTEC in OWL 2 RL. ISWC
insight-ict.eu
Even without any semantics but e.g.
• Conditional probabilistic analysis of stateevolutions in traffic network graphs
• Multiagent (vehicle) path planning
46
Showcase: MyMedia App
P2P network
• Semantic P2P search & live streaming in one app
• Resource-adaptive streaming for HTTP with MPEG-DASH standard
Real mobile-to-mobile search and sharing of live recodings and videosat social events (festivals, sports parades, theme parks) with friends.
47
Video Semantics
Name: ID1_vds.owl
ProfilehasDescription:
“Keanu Reeves in front oft Cinestar!“
hasInput: -hasOutput: LiveStream,
Topic: Actor, CinemahasPrecondition: -hasEffect: -
GroundingAndroid Activity „Play“
REST InterfaceVLC Player
http:// …/ID1_vds.mpd
Semantics: Service in OWL-S
User tagsfrom imported
ontologyin OWL2
User comment
Klusch, M.; Kapahnke, P. (2012): The iSeM Matchmaker. Web Semantics, 15, Elsevier
Semantic video relevance:
Hybrid semantic service matching (logic, text, structural similarity-based)
Content: MPEG-DASH file
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P2P Search & Streaming
User selects video v from returned top-n vids
(2) P2P live streaming of v (with pDASH)
• Initiate P2P streaming session of peer group G for v• Get MPEG-DASH media profile descriptor of v with peer resource info
from each peer p‘ known to p in G
• Make decision: From which p‘ to best download which next segment(s) of v wrt. maximum available resources for each of them ?
• Parallel download & play segments of v from target peers• Update MPD(v); (4)
D: Experts C, B for q; C: Experts E, F for q
Each MyMedia peer p
(1) Semantic expert-driven P2P searchfor given query q (with S2P2P)
49
Performance & User Acceptance
Android 3, Java• @sourceforge.net/projects/mymedia-peer/• pDASH @Uklagenfurt
M Klusch et al. (2014): MyMedia. ACM Intern. Conf. on Mobile and Ubiquitous Computing (Mobiquitous)
User tests: 50 in groups of 2-10; TIFF WLAN; HTC One, Nexus7, S2/S3/S4
• Easy to use; improved user experience
• Issues: Live movie piracy out of theatre; no secured user profiles
Experimental setting: P2P network w/ RPL topology, 1M peers, TTL=10, k=2; uniform at random/sparse distribution of 400 vids,1.4-6Mbps bitrates
• P2P search: AP 0.80, CR10 0.32, AQRT 3s(vs. k-random: 0.35, 0.22, 1.6s, MsgOvh -1.22)
• P2P streaming: 4s avg. latency, BW savings 25%
• Energy [Ws]: Search 1.4, Record 575, Stream 644
Up to 4.7 hrs of usage on Samsung S3
2013, 2014
50
Other FIA Areas for Agents and Semantics …
Smart HomePersonal agent on mobile app interacts with IP-connectedsmart appliances at home
• Remote coordination and execution control of human commands
• Proactive sensing, semantic reasoning and action planningin unprecedented situations at home
Smart home market uncertain: User acceptance still low.
Smart Micro-Grids• Dynamic distributed optimization of energy consumption
• Individual or group rational energy trading and sharingin consumer agent coalitions
- with specialized services and tarifs by provider
51
Challenges
Agents for self-driving cars• Decision-making and navigation based on OWN
semantic perception and understanding of environment
Vs. exact semantic city street map (incl. all traffic signs, lights) produced, updated, distributed by manufacturer
Vs. Human irrational interference, well-being, legal liability
Agents for elderly care
2015 - 2020: Audi A7 „Jack“, Volvo, Ford,Mercedes F015, Tesla, Google, Rinspeed, etc.
Fast building of trusted relationship by human and(non-stupid) humanoid care agent required
Semantics of emotional stances, interaction, etc.
vrworld.com 2015
vrworld.com 2015
52
Social Challenges
… may attract ever more manufacturing companies and public servicesto become vertically/horizontally connected in the FI.
Internet of Everything (IoE) Market Value; Source: CISCO report 2013
Prognosed excessiveIoT/IoS market value …
How to keep them secure?
How to keep the related critical infrastructuresof our public life secure?
53
Social Challenges
As intelligent agent-based and semantics-empoweredapplications are ever more invisibly permeating our everyday life:
How to balance our increasing dependencyon them with our human nature ofself-determination, and our social life ?
Joseph Weizenbaum (1923 – 2008)AI pioneer and critic
Privacy of human communication in the FI ?
Societal response to FIA inflictedjob losses and creation ?
Report 2014
In remembrance of:
54
Take-Home Messages
Intelligent agents and semantic technologies aregeneric key enablers for building FI applications.
Agents for intelligent human-agent interactionand distributed action coordination.
Semantic technologies for semantic interoperation,semantic analysis and explanationof data, streams, processes, and actions.
Combination of both can be quite powerfulin many FIA domains.
„Good-enough“ flexible, fast semantic reasoners andself-coordinating agents on resource-constrained devices.
Incorporation of privacy, trust, and security in practice.
55
Special Thanks
Intelligent Information Systems (I2S) Research Team@ DFKI Agents and Simulated Reality Department, Saarbrücken
Manuel Anglet Patrick KapahnkeXiaoqi Cao Christian MathieuJosenildo Costa Da Silva Luca MazzolaAndreas Frische Hanna MousaMaximilian Harz David WeissAnkush Meshram Ingo ZinnikusMatthias Klusch (head)
Visit us at www.dfki.de/~klusch/i2s
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