Lecture 2 - Intelligent Interfaces II, Faculty of ICT, University of Malta

1. ICS3211 - Intelligent
Interfaces II
Combining design with technology for effective human-
computer interaction
Week 2
Department of Intelligent Computer Systems,
University of Malta,
2016

2. Theoretical Frameworks in
HCI
Week 2 overview:
• Intelligent Interfaces - a recap
• Need for Intelligent interfaces
• Difference between Intelligent Interfaces vs Interfaces
for an intelligent system;
• Components of Intelligent Interfaces
• Human Information Processing - methods & models;

3. Learning Outcomes
At the end of this session you should be able to:
• Describe and recognise intelligent interfaces;
• Explain the difference between an intelligent interface and an
interface for an intelligent system;
• List the components for Intelligent Interfaces
• Compare different theories and their applications to the field of
HCI;
• Compare different information processing methods and models;

4. Recap
• What have we discussed during the past week?
• What important points have you acquired through
the online discussions?
• Describe your online post input in more detail.
• Think of an intelligent interface; discuss with 2 other
class members why it falls under the category of
intelligent interfaces

5. Intelligent Interfaces
• What are they?
• Intelligent interfaces have two components:
✴ArtificiaI Intelligence (AI)
✴Computer Human Interaction (CHI)
• Not every intelligent program/system has an
intelligent interface. Why?

6. IUI Research Fields

7. A normal user interface is defined as a method of
communication between a human user and a
machine. If we extend this definition, we can say that
an intelligent user interface uses some kind of
intelligent technology to achieve this human-machine
communication. In other words, IUIs are interfaces
with the ability to adapt to the user, communicate with
the user, and solve problems for the user.

8. “Intelligent user interfaces specifically aim to enhance
the flexibility, usability, and power of human-computer
interaction for all users. In doing so, they exploit
knowledge of users, tasks, tools, and content, as well
as devices for supporting interaction within differing
contexts of use.”
[Maybury 2001]

9. • Why do we need an intelligent interface?
• Interfaces can get too complex
• Interfaces can get too inflexible
• Interfaces don’t change when our needs change
• Interfaces don’t work with each other

10. • What makes an interface intelligent?
• Adapts to different user needs
• Learns new concepts and techniques
• Anticipates users’ needs
• Takes initiative and makes suggestions to user
• Justifies actions taken

11. Difference between an Intelligent System
and an Intelligent Interface

12. • How can user interfaces engage users to act more
intelligently?
• What empirical knowledge regarding the presentation of
information can be applied in the design of user interfaces
and dialog models?
• How will users perceive and accept the evolving ability of
computers to perform surrogate tasks correctly?
• What are current and future potential models of interactive
computing?
• How useful are sophisticated reasoning and knowledge-
base technologies in UI design?

13. Components of Intelligent
Interfaces
• The User Model
• Multimodal
Communication
• Plan recognition
• Dynamic
Presentation
• Natural Language
• Intelligent Help
• Interface Adaptability

14. User Model
• Information which best describes the user, and
which is used to determine how the data is
presented;
• Best used when:
• system seeks to adapt behaviour to users;
• class of users is diverse

15. • Uses of user models:
• getting input from user;
• providing help & advice;
• understanding user’s information seeking
behaviour;
• providing output to user;
• deciding what to say & how to say it;

16. Multimodal Communication
• Uses of various methods of communication with an
interface
• Two purposes:
• enables users to use system more intuitively;
• gives users more freedom;

17. Plan Recognition
• Deduces what user plans to do;
• Takes into consideration:
• system knowledge
• user model;
• user’s actions;

18. Dynamic Presentation
• Different people view data in different ways;
• System decides to display data based on user
models;
• Intelligent data displays from data entered in system

19. Natural Language
• High degree of freedom on the user’s part;
• Adds more intuitiveness to a system;
• Makes system less cumbersome;

20. Intelligent Help
• Help that is presented to the user, just in time;
• System recognises that the user is facing a problem
and suggests ways in which s/he can solve the
problem

21. Interface Adaptability
• System may adapt to user preferences without the
user having to specify;
• Interface may adapt depending on user models
• Step towards response planning

22. HCI Theories
Early theories & Models
• GOMS Model
• Goals
• Operations
• Methods
• Selection Rules

23. Theories that span 3 major
eras
• Theories that view human-computer interaction as information
processing;
• Theories that view interaction as the initiative of agents pursuing projects;
• Theories that view interaction as socially and materially embedded in rich
contexts;

24. Information Processing:
methods & models
• HIP approach tied to cognitive psychology, human
factors, and human engineering;
• Empirical studies evaluate the information
processing requirements of various tasks in which
humans use computers;
• Computation models are developed with the intent
to characterize human information processing when
interacting with computers and to predict human
performance with alternative interfaces;

25. Information Processing
Methods
• Signal detection methods and theory
• Chronometric methods
• Speed-accuracy methods
• Psychophysiological methods

26. Information Processing
Models
• Discrete vs. continuous:
• representation;
• transformation;
• transmission;

27. Information
Processing
Stages
for choice reaction tasks
where each stimulus is
assigned to a unique
response

28. Information Processing in
Choice-Reaction Tasks
• Stimulus Identification:
• preprocessing;
• feature extraction;
• identification;
• Response Selection;
• Response Execution:
• motor programming;
• motor adjustment;

29. Attention Models
• The Filter Theory;
• The Filter Attenuation
Theory;
• Load Theory;
• Unitary Resource
Model;
• Multiple Resource
Model;
• Feature Integration
Theory;
• Response Selection
Bottleneck Theory;

30. –Jef Raskin
“As far as the customer is concerned, the
interface is the product.”