Artificial Intelligence Tutorial 

The Artificial Intelligence Tutorials (AIT) 2010 is a world class tutorial session organised by MIMOS berhad on 26th and 27th July 2010. In this event, we are privileged to have EIGHT invited experts on Artificial Intelligence (AI), specialised in Semantic Technology from USA, Austria, Germany, Hong Kong, France, and Malaysia. The purpose of the tutorials is to promote world class research and development in AI. The tutorials are organized jointly by MIMOS BERHAD and the Center of Excellence in Semantic Technology and Augmented Reality (CoE-STAR, UNIMAS). It will be co-located with the 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010) , the Semantic Technology And Knowledge Engineering Conference (STAKE 2010)a and the International Conference on Conceptual Structures(ICCS 2010) at Damai Beach Resort, Kuching, Sarawak, Malaysia.
 

Tutorials

 

Tutorial 1
Presenters: Dr. Sebastian Rudolph
Title: Semantic Web Ontology Languages
Description: Ontology languages for the Semantic Web are based on paradigms from knowledge representation and reasoning. The Resource Description Framework RDF and its more expressive counterpart RDF Schema are closely related to semantic networks and existential graphs. The Web Ontology Language OWL is based on description logics. Both languages are a recommended standard by the World Wide Web Consortium (W3C) for modeling ontologies on and for the Semantic Web.
This tutorial introduces RDF, RDF Schema, and OWL in very detail. It covers web-enabled syntax based on XML, their formal semantics, logical counterparts, and established inference techniques including description logic tableaux calculi. The tutorial discusses these issues in the context of the broad Semantic Web vision, including many examples, recent applications, and available tools.
 

Tutorial 2
Presenter: Prof. Michel Chein, Prof. Madalina Croitoru, Alain Gutierrez, Marie-Laure Mugnier
Title: Graph-based Knowledge Representation with COGUI
Description: Theoretical foundations and practical demonstration of (1) Basic Conceptual Graph Model, (2) Rules and (3) RDF(S) interoperability.
 

Tutorial 3
Presenter: Prof. John Sowa
Title: Integrating Semantic Systems
Description: Everything implemented on a computer has semantics that is meaningful to the implementers and the users. Problems arise when different systems and the data they process have different semantics. More problems arise when the users and implementers make different assumptions about the semantics. Similar problems arise as systems evolve over time with updates, revisions, extensions, and connections to independently developed systems. Computer systems have been successfully interacting across long distance networks for over forty years. But a tight integration, even of local systems, is hard to achieve because of the difficulty of ensuring that all components will interpret the same data in the same way. Further complications are created by different notations and conventions in databases, knowledge bases, the Semantic Web, folksonomies, and legacy systems that have no explicit semantic notations. Finally, even the most precisely defined and integrated semantic systems must interact with people who have little or no knowledge of the precise definitions and little time, desire, or ability to learn them.

This tutorial will use examples and case studies to illustrate these problems, various ways of addressing them, and the challenges for the future. Following are the topics covered:
  • Examples of semantic systems and their methods for representing and using logic and ontology
  • Aligning and relating different ontologies expressed in the same notation or different notations
  • Methods of communication and control among loosely coupled systems and more tightly integrated systems
  • Diagrams and controlled natural languages for expressing formal ontologies in humanly readable forms
  • Methods for extracting the implicit ontologies and terminologies from legacy systems and documents that have no semantic annotations
  • Supporting interoperability among heterogeneous systems with different semantic representations, including legacy systems with no explicit semantics
 

Tutorial 4
Presenter: Prof. Eric Tsui
Title: Taxonomy & Folksonomy
Description: This tutorial will introduce the concept of taxonomy and outline the methodologies and challenges in the creation and maintenance of corporate taxonomies. It will also explore the relationship between taxonomies and search strategies. Advancement in Web 2.0 technologies has lead to the emergence of user-tags and folksonomies. This tutorial will also address the interplay between these two opposing approaches to information classification as well as showcase TaxoFolk, a tool that generates hybrid taxonomy-folksonomy for enhancing knowledge navigation. Whenever, appropriate local and overseas case studies will be used to help reinforce the concepts.
 

Tutorial 5
Presenters: Teresa Liew
Prof. Eric Tsui
Title: Scenario-Based Authoring and Execution in a Blended Learning Environment
Description: This tutorial will explain the nature and power of Scenario-Based Learning (SBL) for blended learning. It will include a review of the scenario-based and rapid E-Learning toolkits in academia and the market place. In particular, participants will have hands on experience with RAPIDS, a Scenario-based E-Learning tool with competency profiling capabilities developed by the Knowledge Management Research Centre, The Hong Kong Polytechnic University and experience the ease and swiftness in authoring and deploying scenarios in an online learning environment. Participants are required to bring their own laptop and will complete the tutorial with a copy of RAPIDS as well as their self-created animated scenario(s).
 

Tutorial 6
Presenter: Prof. Andreas Dengel
Title: 1+1 = 2+x: Socio-Technical Collaborative Intelligence
Description: Knowledge derived from collaborative efforts is a major competitive factor for any ecosystem. Enterprises all over the world put a lot of efforts on the development of software systems acting as knowledge-based complements to human creativity and intuition and aiming at the increase of the organizational intelligence. As humans and computers more and more are part of socio-technical memories which together build the collaborative problem solving capability, the role of anticipating, context-adaptive technology is becoming more and more important. In this tutorial, I like to address the most important methods and models needed to establish socio-technical collaborative intelligence, give examples how these techniques may be applied for information sharing, search, and innovative knowledge management services.
 

Tutorial 7
Presenter: Dr. Bong Chin Wei
Title: Multi-objective Nature-inspired Techniques for Image Segmentation
Description:

Image segmentation has long been an important and challenging topic in the field of digital image processing. It is a process of partitioning an image into several disjointed regions that are homogeneous with regards to some measures that subsequent higher level computer vision processing, such as object recognition, image understanding and scene description can be performed. It is also considered as the most difficult low-level task because the segmentation performance needs to be adapted to the changes in image quality because it is affected by variations in environmental conditions, imaging devices, time of day, and so on. A new trend of problem formulation for image segmentation is to use approaches based on multiple objectives in its decision making process.

The purpose of this tutorial is to introduce the modeling of Multi-objective Nature-inspired Techniques for Image Segmentation. The topics covered include the following:

  • How image segmentation is a problem with multiple objectives
  • Multiple objectives associated with image segmentation problems
  • Characteristics of multi-objective optimization model critical for the image segmentation decisions
  • A list of Multi-objective Nature-inspired Techniques methods that has been applied in image segmentation problem.
  • Design issues and further direction
 

Tutorial 8
Presenter: Prof. Brian Garner
Title: Blended Learning Tutorial
Description:

The explosion of interest in Blended Learning, thereby extending the scope and value of e-Learning, is best understood in terms of the Vocational requirements of Business , Governments and individuals/consultants, particularly the Professional classes. In this Tutorial the Corporate perspective is first addressed, including demographic influences, so that the Tutorial objectives may be clearly understood in terms of technology trends and organisational learning requirements.

The topics addressed may be summarised as:

  • Objectives, Frameworks and Process in building Blended Learning Systems
  • ‘Who are the Players’, including Suppliers of Educational Resource materials
  • Technology Drivers in Blended Learning and its management.
  • Role of Facilitators, Mentors and Virtual Partners in achieving Successful Outcomes
  • Progress with Knowledge-mediated Learning
  • Trend in Blended Learning Frameworks for Self-directed Learners
  • Transformational Leadership Objectives in Mobile Education
Naturally, strong interaction with participants is a primary goal of the Tutorial to ensure an effective understanding of the principles and practice of Blended Learning!

 

Tutorial 9
Presenter: Professor Jerome Lang
Title: Computational social choice
Description:

Social choice theory aims at constructing and analyzing methods for collective decision making; it is an important branch of mathematical economics. Here are a few examples of collective decision making: an election; a group of friends deciding about the program of a common holiday; fair division of resources (for instance, allocating goods in a divorce settlement, allocating classes and time slots in a high school); a jury agreeing on a verdict. Until now, social choice theory has focused on axiomatic issues and has somewhat neglected computational issues: the problem is generally considered to be solved when the existence (or the non-existence) of a procedure meeting some requirements has been shown.

Here is where computer science (and more specifically Artificial Intelligence and Operations Research) comes into play. From 10 years or so, a new research community called "computational social choice", at the boarder between social choice theory and computer science, has been rapidly developing. The tutorial will give an overview of the main topics addressed in computational social choice, including the exact and approximate computation of computationally hard voting rules, voting on combinatorial domains, computational barriers to strategic behaviour, incomplete knowledge and communication requirements in voting, and computational issues in fair division.