PGNET, Liverpool JMU, June 2005 MediaHub: An Intelligent MultiMedia Distributed Platform Hub Glenn Campbell, Tom Lunney, Paul Mc Kevitt School of Computing and Intelligent Systems Faculty of Engineering University of Ulster, Magee Campus Northland Road, Derry {Campbell-g8, TF.Lunney,

PGNET, Liverpool JMU, June 2005 Outline Goals and objectives Key research problems Distributed Processing Distributed Platforms Architecture of MediaHub Decision making in MediaHub Comparison to related research Tools and future development

PGNET, Liverpool JMU, June 2005 Goals The primary objectives of MediaHub are to: Interpret/generate semantic representations of multimodal input/output Perform fusion and synchronisation of multimodal data (decision-making) Implement and evaluate a multimodal platform hub (MediaHub)

PGNET, Liverpool JMU, June 2005 Goals Research questions: Semantic representation? Communication with other elements of a platform? Semantic representation? Decision-making?

PGNET, Liverpool JMU, June 2005 Key research problems Semantic Representation Represent language and vision Frames or XML? Semantic Storage Blackboard model? Non-blackboard model? Decision-making Fusion and synchronisation AI technique

PGNET, Liverpool JMU, June 2005 Frames (CHAMELEON) (Brøndsted et al. 1998, 2001) [MODULE INPUT: input INTENTION: intention-type TIME: timestamp] [SPEECH-RECOGNISER UTTERANCE:(Point to Hanne’s office) INTENTION: instruction! TIME: timestamp] [GESTURE GESTURE: coordinates (3, 2) INTENTION: pointing TIME: timestamp] XML (M3L, SmartKom) ( Bühler et al. 2002, Wahlster et al. 2001) list epg_browse now T19:42: T22:00: T19:50: T19:55:00 Today’s Stock News ARD …….. Semantic representation

PGNET, Liverpool JMU, June 2005 Semantic storage Blackboard or Non-blackboard? High coupling – Blackboard? Low coupling - distributed architecture? Communication Via central blackboard? Message passing between modules?

PGNET, Liverpool JMU, June 2005 Decision-making (fusion & synchronisation) Rule-based Potential for Other AI techniques Fuzzy Logic Neural Networks Genetic Algorithms Bayesian Networks (CPNs)

PGNET, Liverpool JMU, June 2005 Distributed processing DACS (Fink et al. 1995, 1996) Open Agent Architecture (OAA) (Cheyer et al. 1998, OAA 2004) JATLite (Kristensen 2001, Jeon et al. 2000) JavaSpaces (Freeman 2004) CORBA (Vinoski 1993).NET (Fay 2003)

PGNET, Liverpool JMU, June 2005 Intelligent Multimedia Distributed Platforms Blackboard Model: Ymir (Thórisson 1999) CHAMELEON (Brøndsted et al. 1998, 2001) Smartkom (Bühler et al. 2002, Wahlster et al. 2001, SmartKom 2004) DARBS (Nolle et al. 2001) DARPA Galaxy Communicator (Bayer et al. 2001) Psyclone (Psyclone 2004) Spoken Image/SONAS (Ó Nualláin et al. 1994, Ó Nualláin & Smith 1994, Kelleher et al. 2000)

PGNET, Liverpool JMU, June 2005 Intelligent Multimedia Distributed Platforms Non-blackboard Model: WAXHOLM (Carlson et al. 1996) AESOPWORLD (Okada 1996) COLLAGEN (Rich et al. 1997) INTERACT (Waibel et al. 1996) Oxygen (Oxygen 2004) EMBASSI (Kirste 2001, EMBASSI 2004) MIAMM (MIAMM 2004)

PGNET, Liverpool JMU, June 2005 CHAMELEON Language & vision integration system consists of ten modules, mostly programmed in C and C++ DACS communication system used for communication Blackboard stores semantic representations produced by other modules Communication between modules achieved by exchanging semantic representations between themselves or blackboard Semantic representation in form of input, output and integration frames

PGNET, Liverpool JMU, June 2005 Architecture of CHAMELEON

PGNET, Liverpool JMU, June 2005 SmartKom User adaptive interface for human-computer interaction Mobile Public Home/Office Facilitates speech, gestures and facial expression input XML-based mark-up language, M3L, used for semantic representation Distributed multiple blackboard model

PGNET, Liverpool JMU, June 2005 Architecture of SmartKom

PGNET, Liverpool JMU, June 2005 Dialogue Manager Acts as a blackboard module Facilitates communication between other modules Synchronisation Semantic Representation Database Provides semantic representation of language and vision data Decision Making Module AI technique for a unique form of decision-making Bayesian Networks (CPNs) Neural Networks, Genetic Algorithms, Fuzzy Logic Architecture of MediaHub

PGNET, Liverpool JMU, June 2005 Architecture of MediaHub

PGNET, Liverpool JMU, June 2005 Decision Making Module

PGNET, Liverpool JMU, June 2005 Decision making in MediaHub Decisions at Input: Determining semantic content of input Fusing semantics of input (into frames/XML) Resolving ambiguity at input Decisions at Output: Synchronising language with visual output Best modality for output (i.e. language or vision)

PGNET, Liverpool JMU, June 2005 Input example “Copy all files from the ‘process control’ folder of this computer to a new folder called ‘check data’ on that computer”.

PGNET, Liverpool JMU, June 2005 Output Example P T “This is the best route from Paul’s office to Tom’s office”.

PGNET, Liverpool JMU, June 2005 Comparison to related research

PGNET, Liverpool JMU, June 2005 Potential Tools Main Programming Language Java C++ Communication.NET DACS Semantic Representation XML XHTML + Voice SMIL RDF Schema MPEG-7 EMMA

PGNET, Liverpool JMU, June 2005 Potential Tools Decision Making Tools HUGIN GUI / API (Hugin 2004) Microsoft MSBNx / MSBN3 (Kadie et al. 2001) GeNIe/SMILE (Genie 2005) Netica (Norsys 2005) Bayes Net Toolbox (BNT 2005) BUGS (BUGS 2005)

PGNET, Liverpool JMU, June 2005 Hugin Tool for implementing Bayesian Networks as CPNs (Causal Probabilistic Networks) Hugin GUI Graphical user interface to Hugin decision engine Hugin API Library implemented in C, C++, Java Allows programs to implement Bayesian Networks for decision making

PGNET, Liverpool JMU, June 2005 Bayesian Networks AKA Bayes nets, Causal Probabilistic Networks (CPNs), Bayesian Belief Networks Consists of nodes and directed edges between nodes Node represents a variable Edge represents cause-effect relationship An edge connecting two nodes A and B indicates a direct influence exists between state of A and the state of B

PGNET, Liverpool JMU, June 2005 Simple Bayesian Network ‘Diet’ and ‘Exercise’ nodes have influence over ‘Weight Loss’ node

PGNET, Liverpool JMU, June 2005 Future development Define necessary decisions Develop Bayesian decision making using Hugin API for Java Semantic storage Communication Semantic representation scheme Semantic representation database Acquire multimodal corpora for testing Test MediaHub in an existing Multimodal Platform e.g. CONFUCIUS (Ma & Mc Kevitt 2003)

PGNET, Liverpool JMU, June 2005 Conclusion An intelligent multimodal distributed platform hub called MediaHub will be developed MediaHub will interpret and generate semantic representations of multimodal input and output MediaHub will perform fusion and synchronisation of language and vision data MediaHub will provide a new method of decision making within a distributed platform hub MediaHub will be tested within an existing multimodal platform (e.g. CONFUCIUS)