Intelligent Agents and Agent Environment

1. AI in Smart
Environments
ARI2132
vanessa.camilleri@um.edu.mt

2. Topic 4: Overview
• Agents & Intelligent Agents
• Agent Models
• Benefits of user adaptivity
• Usability challenges
• Collecting data from users
• Future needs in IUI’s
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3. Agents & Intelligent Agents
• Rational
• Autonomous
• Uses a perception of the environment status to take
decisions
• Uses and provides functions

4. Agent Models
• Simple Reflex
• Model-based Reflex
• Goal-based
• Utility-based
• Learning

5. Agent Environment Space
• Observable
• Deterministic
• Dynamic
• Discrete

6. Agent Interactions
• Situated
• Social
• Simulated

7. Intelligent Interfaces
Why design them?
• To improve communication between humans and
computers.
• To enhance the flexibility, usability, and power of
human-computer interaction for all users.
HCI scientists exploit knowledge of users, tasks,
tools, and content, as well as devices for supporting
interaction within different contexts of use.
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8. In simple terms, an intelligent interface provides a way
for a system to learn something about each individual
user and adapt its behaviour to them in some nontrivial
way.

9. • Amazon adapts its recommendation system to the
user’s previous history of purchase. Depending on
their function and form, systems that adapt to their
users have been given labels ranging from
adaptive interfaces through user modelling
systems to software agents or intelligent agents.
• However a common property binding these
systems or agents is user-adaptivity

10. Systems where the
intelligence lies mainly in UIs
• Systems with adaptive user interfaces that are automatically
adapted to the inferred capabilities or needs of the user.
• Multimodal systems that aim to enable more natural, human-like
forms of input and output.
• Systems with human-like virtual characters that enable the user to
interact with a system in a way that is partly similar to human-
human interaction.
• Smart environments in which embedded objects interact
intelligently with their users.
• Personalised websites, in which the displayed content is adapted
to the inferred interests of the user.

11. Systems where the intelligence
lies mainly behind UIs
• Recommender systems, which present products, documents, or other items that
are expected to be of interest to the current user.
• Systems that employ intelligent technology to support information retrieval.
• Learning environments that offer learning assistance on the basis of assessments
of each learner’s capabilities and needs.
• Interface agents that perform complex or repetitive tasks with some guidance from
the user.
• Situated assistance systems that monitor and support a user’s daily activities.
• Systems for capturing knowledge from domain experts who are not knowledge
engineers.
• Games that make use of AI technology to create the opponents against which the
human players play.

12. General schema for the processing in a user adaptive
system
(Dotted arrows: use of information; solid
arrows: production of results.)

13. • A user-adaptive agent system can be defined as:
An interactive system that adapts its behaviour to
individual users on the basis of processes of user
model acquisition and application that involve
some form of learning, inference, or decision
making

14. Overview of adaptation in amazon

15. Interface pro-activity continuum; moving
towards a completely automated and intelligent
task completion

16. Benefits of user-adaptivity:
Functions: supporting system use
• Taking over parts of routine tasks;
• Adapting the interface;
• Helping with system use;
• Mediating interaction with the real world;
• Controlling a dialog;

17. Benefits of user-adaptivity:
Functions: supporting information acquisition
• Helping users find information;
• Recommending products;
• Tailoring information presentation;
• Supporting collaboration;
• Supporting learning;

18. Usability challenges for user-adaptive systems

19. Strategies for dealing with
tradeoffs among usability goals in
user-adaptive systems

20. Obtaining information about users:
• Explicit self-reports & assessments;
• self reports about objective personal
characteristics;
• self assessments of interests & knowledge;
• self reports on specific evaluations;
• responses to test items;

21. Obtaining information about users:
• Non explicit input;
• naturally occurring actions;
• previously stored information;
• low levels of psychological states;
• signals concerning the current surroundings;

23. Use of Data Collected
• The key difference between user-adaptive systems and
other interactive systems is the inclusion of some
method for acquiring and exploiting a user model.
• What is needed are (a) some implementation of the
adaptation algorithm, not necessarily embedded in any
interactive system; and (b) a database of behavioural
data from a number of users who have used a relevant
nonadaptive system. The researcher can then apply the
modelling method to the data in order to determine how
well the system would adapt to the users in question.

24. Future of User-adaptive
Systems
• Growing need for user-adaptivity;
• Diversity of Users and Contexts of Use
• Number and Complexity of Interactive Systems
• Scope of Information to Be Dealt With

25. Future of User-adaptive
Systems
• Increasing Feasibility of Successful Adaptation
• Ways of Acquiring Information About Users
• Advances in Techniques for Learning, Inference,
and Decision
• Attention to Empirical Methods