CA 7B3SOFTWARE AGENTS

INTERACTING WITH AGENTSAGENT FROM DIRECT MANIPULATION

TO DELEGATION

Presenters:BHEEMRAJ SAINI – 205113009SRISHTI GUPTA – 205114001AJAY BENIWAL – 205114002

AKHILESH CHOUDHARY – 205114003PARAKH KUMAR RAHANGDALE – 205114004

Date: July 18, 2016

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.”

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]

Agents’ PropertiesAgents’ Properties

[Brenner et al., 1998]

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)

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

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

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

CharacteristicsCharacteristicsCooperation (Proactive)

Autonomy Adaptation(Learning)

Smart agent

[Nwana, 1996]

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

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

Autonomy• Agents act

autonomously to pursue their agenda.– Goal directed

behavior

Cooperation

Autonomy Adaptation

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

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

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

Example: Agent = Medical Diagnosis Example: 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)

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.

SafetyMaking 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

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.

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.

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

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

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

Agent from Direct Manipulationto 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

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

Steps in DelegationI – 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

Introduce the Task

• Determine the task to be delegated

• Determine the tasks to retain

• Select the delegate

Demonstrate Clearly

• Show examples of previous work

• Explain objectives• Discuss timelines, set

deadlines

Ensure Understanding

• Clear communication• Ask for clarification• Secure commitment

Allocate…authority, information, resources

• Provide access to all information sources• Provide appropriate training to ensure

success

Let go…

• Communicate delegate’s authority• Step back, let them work

Support and Monitor

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

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