1. CA 7B3
SOFTWARE AGENTS

2. INTERACTING WITH AGENTS
AGENT FROM DIRECT MANIPULATION
TO DELEGATION
Presenters:
BHEEMRAJ SAINI – 205113009
SRISHTI GUPTA – 205114001
AJAY BENIWAL – 205114002
AKHILESH CHOUDHARY – 205114003
PARAKH KUMAR RAHANGDALE – 205114004
Date: July 18, 2016

3. What is an Agent?What is an Agent?
• “An agent is anything that can be viewed
as perceiving its environment through
sensors and acting upon that environment
through effectors.”

4. Formal DefinitionsFormal Definitions
• “Autonomous agents are computational
systems that inhabit some complex dynamic
environment, sense and act autonomously in
this environment, and by doing so realize a
set of goals or tasks for which they are
designed.” [Maes, 1995]
• “Intelligent agents continuously perform three
functions: perception of dynamic conditions in
the environment; action to affect conditions in
the environment; and reasoning to interpret
perceptions, solve problems, draw
inferences, and determine actions.” [Hayes-
Roth, 1995]

5. Agents’ PropertiesAgents’ Properties
[Brenner et al., 1998]

6. ReactivityReactivity
• A reactive system is one that maintains an
ongoing interaction with its environment,
and responds to changes that occur in it
(in time for the response to be useful)

7. ProactivenessProactiveness
• Reacting to an environment is easy (e.g., stimulus
 response rules)
• But we generally want agents to do things for us
• Hence goal directed behavior
• Pro-activeness = generating and attempting to
achieve goals; not driven solely by events; taking
the initiative

8. Social AbilitySocial Ability
• The real world is a multi-agent environment:
we cannot go around attempting to achieve
goals without taking others into account
• Some goals can only be achieved with the
cooperation of others
• Social ability in agents is the ability to interact
with other agents (and possibly humans) via
some kind of agent-communication language,
and perhaps cooperate with others

9. Other PropertiesOther Properties
• Mobility: the ability of an agent to move around an electronic
network
• Veracity – honesty: an agent will not knowingly communicate false
information
• Benevolence – kind: agents do not have conflicting goals, and that
every agent will therefore always try to do what is asked of it
• Rationality: agent will act in order to achieve its goals, and will not
act in such a way as to prevent its goals being achieved - at least
insofar as its beliefs permit
• Learning/adaption: agents improve performance over time

10. CharacteristicsCharacteristics
Cooperation (Proactive)
Autonomy Adaptation
(Learning)
Smart agent
[Nwana, 1996]

11. Adaptation
• Agents adapt to their
environment and
users and learn from
experience.
– Via machine learning,
knowledge discovery,
data mining, etc.
– Situated in and aware
of their environment
Cooperation
Autonomy Adaptation

12. Cooperation
• Agents use standard
languages and protocols
to cooperate and
collaborate to achieve
common goals.
– Cooperate with human
agents and other
software agents
– Supported by agent
communication
languages and protocols.
– Consistent with human
conventions and intuition.
Cooperation
Autonomy Adaptation

13. Autonomy
• Agents act
autonomously to
pursue their agenda.
– Goal directed
behavior
Cooperation
Autonomy Adaptation

14. Task Modeling in Intelligent AgentsTask Modeling in Intelligent Agents
• Before we design an intelligent agent, we
must specify its “task environment”:
PEAS:
Performance measure
Environment
Actuators
Sensors

15. PEAS Description
of Task Environments
Performance
Measures
Environment
Actuators
Sensors
used for high-level characterization of agents
determine the actions the agent
can perform
surroundings beyond the
control of the agent
used to evaluate how well an
agent solves the task at hand
provide information about the
current state of the environment

16. Example: Agent = Taxi DriverExample: Agent = Taxi Driver
• Performance measure: Safe, fast, legal,
comfortable trip, maximize profits
• Environment: Roads, other traffic,
pedestrians, customers
• Actuators: Steering wheel, accelerator,
brake, signal, horn
• Sensors: Cameras, speedometer, GPS,
odometer, engine sensors

17. Example: Agent = Medical DiagnosisExample: Agent = Medical Diagnosis
SystemSystem
• Performance measure: Correct Diagnosis,
minimize costs
• Environment: Patient, hospital, staff
• Actuators: Screen display (questions,
tests, diagnoses, treatments, referrals)
• Sensors: Keyboard (entry of symptoms,
findings, patient's answers)

18. How Might People Interact with
Agents?
How will intelligent agents interact with
people and perhaps more important, how
might people think about agents?
Agents might set up schedules, reserve hotel
and meeting rooms, arrange transportation,
and even outline meeting topics, all without
human intervention.

19. Safety
Making sure that the agent does not do things that
would jeopardize the physical, mental, or
monetary well-being of the owner.
 The sense of control, but because the technical
and social implications are considerably
different, it deserves its own special
consideration.
 Privacy and confidentiality of actions will be
among the major issues confronting the use of
intelligent agents in our future of a fully
interconnected, fully communicating society.
Privacy

20. Human-Agent Interaction
The manner by which the person's
conceptual model of the agent's method
of operation and activities is presented,
and the manner by which the agent offers
advice and information to the person.

21. What is Delegation?
Delegation is the assignment of responsibility to
another person for the purpose of carrying out
specific job-related activities. Delegation is a
shift of decision-making authority from one
organizational level to another.

22. Elements of Delegation
–a 'principal'
• An individual who delegates
authority over a particular policy
area (or function) to another
–an 'agent'.
• The “other” person who receives the
delegated authority

23. Knowing When to Delegate
Delegating can be especially helpful in the
following situations:
– When the task offers valuable training to an
employee
– When an employee has more knowledge or
experience related to the task than you
– When the task is recurring and all employees should
be prepared or trained
– When the task is of low priority and you have high
priority tasks that require your immediate attention

24. To Whom Should You Delegate?
When deciding who to select for the task, you
must consider:
– The current work load of the employee
– The employee’s strengths and weaknesses
– The training and experience levels of the
employee

25. Agent from Direct Manipulation
to Delegation
• Delegation gives users the option to offload tasks
to software systems—agents—that perform the
tasks for the user.
• This enables users to perform tasks that are
difficult to perform using graphical user
interfaces, tasks such as searching and retrieving
data in large distributed networks

26. Benefits of Delegation
• Manager / Supervisor Benefits
– Reduced stress
– Improved time management
– Increased trust
• Employee Benefits
– Professional knowledge and skill development
– Elevated self-esteem and confidence
– Sense of achievement
• Organizational Benefits
– Increased teamwork
– Increased productivity and efficiency

27. Steps in Delegation
I – Introduce the task
D- Demonstrate clearly what needs to be done
E - Ensure understanding
A - Allocate authority, information and resources
L - Let go
S - Support and Monitor

28. Introduce the Task
• Determine the task to
be delegated
• Determine the tasks
to retain
• Select the delegate

29. Demonstrate Clearly
• Show examples of previous
work
• Explain objectives
• Discuss timelines, set
deadlines

30. Ensure Understanding
• Clear communication
• Ask for clarification
• Secure commitment

31. Allocate…
authority, information, resources
• Provide access to all information sources
• Provide appropriate training to ensure
success

32. Let go…
• Communicate delegate’s authority
• Step back, let them work

33. Support and Monitor
• Schedule follow-up meetings
• Review progress
• Assist, when requested
• Avoid interference
• Publicly praise progress and completion
• Encourage problem solving

34. References
[1]: Jeffrey M. Bradshaw, “Software Agents”, MIT Press, 2000
[2]: http://www.javaworld.com/javaworld/jw-04-1997/jw-04-agents.html
[3]: http://www.msci.memphis.edu/~franklin/AgentProg.html
[4]: http://www.javaworld.com/javaworld/jw-04-1997/jw-04-hood.html
[5]: http://www.trl.ibm.co.jp/aglets/JAAPI-whitepaper.html
[6]: http://luckyspc.lboro.ac.uk/Docs/Papers/Mesela97.html
[7]: http://www.javaworld.com/javaworld/jw-05-1997/jw-05-hood.html
[8]: http://www.trl.ibm.co.jp/aglets/whitepaper.htm
[9]: http://www.networking.ibm.com/iag/iaghome.html#new