Assignment 2: Intelligent Agents

Research on the following types of agent programs and discuss them on your own words. (Note: Answers must be enumerated in a numbered or bulleted form but the explanations should at least 2 paragraphs.)

1. Shopping Agents
   It is also called shopping bots in which it was dedicated to purchase commodities and products within the specific sites. Most of commodities available on the internet are raging from paid subscriptions to in game items and cash. Though, the main function of the said module is transaction, cash items, products and invoices. These agents must be secure also to prevent frauds.
   One of the examples is the in game items from Zynga. To purchase those in-game items, it requires in-game cash which requires Paypal or even major credit card to purchase cash. Then use the in-game cash to purchase in-game items.
   
2. Surveillance Agents
   This agent collects information and check the flow of the system. If something is wrong during the flow, it would record it and send it to authorized user who controls it. In which, it is like tracking some information and collecting data while tracking.
   For example, the trucking system that uses maps and nodes representing trucks registered by the company using the tracking device. It will collect information on where is the current location of the truck, the distance between the current location and the garage or the company’s site, distance and time to be travelled and the like.
   

References:
http://en.wikipedia.org/wiki/Software_agent

http://www.sce.carleton.ca/netmanage/docs/AgentsOverview/ao.html
http://www.peterindia.net/SoftwareAgentsView.html

• User Agents (Personal Agents)

User Agents are intelligent agents that performs or acts certain works in your behalf. Example of these agents, automatically checks and sorts emails according to the user's choice and set priority to the important emails arrive. Sometimes these agents are used in computer games to act as an opponent or patrol game areas. It also being used to assemble customized new reports, several version of these are Newshub and CNN. It also being used to facilitate with online job search duties by scanning known job boards and sending the resume to opportunities who meet the desired criteria.

From its definition itself it shows how this intelligent agent do things like the human do especially on the World Wide Web.

• Data Mining Agents

Data Mining agents are intelligent agents with the use of information technology, it find trends and patterns to sort the information they are wanted in the bunch of information from different sources.

It is a software program built for the purpose of finding information effectively. It is a type of intelligent agent that operates in a data warehouse and attempts to find relationships between different pieces of data. This type of agent is able to detect major trend changes, as well as detect new relative information. Once a piece of information is found, it automatically attempts to alert the user.

References:
http://en.wikipedia.org/wiki/Data_mining_agents
http://en.wikipedia.org/wiki/Software_agent

Intelligent Agents are cartoon and life-like characters that appears on a screen that assists users in various task. I would like to point out that the Intelligent Agents that I have researched for the Human - Computer Interaction elective are must be related to Human - Computer Interaction.

People from the Millennium Age are more exposed to Agents that are Visually Sensed. The types of Intelligent Agents are:

1. Synthetic characters
   
   
   
   • Commonly designed as 3D characters.
     
   • Effort goes into designing them to be lifelike, exhibiting realistic human movements
     
   • The design of the characters' appearance, their facial expressions, and how their lips move when talking are also considered important interface design concerns.
     

Emotional agents

• These are designed with a predefined personality and set of emotions that are manipulated by users.
  
• The aim is to allow people to change the moods or emotions of agents and see what effect it has on their behavior.
  
• Controlled through sliders and icons
  

Embodied conversational interface

• Emulating human conversation
  
• This approach is the most anthropomorphic in its aims of all the agent research and development.
  

Animated agents agents

• These are similar to synthetic characters except they tend to be designed to play a collaborating role at the interface.
  
• Typically, they appear at the side of the screen as tutors, wizards and helpers intended to help users perform a task.
  

i remember our HCI last year. We discussed about agents. An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors. A human agent has eyes, ears, and other organs for sensors, and hands, legs, mouth, and other body parts for effectors.

1.Simple reflex agents

Simple reflex agents act only on the basis of the current percept, ignoring the rest of the percept history. The agent function is based on the condition-action rule: if condition then action.

This agent function only succeeds when the environment is fully observable. Some reflex agents can also contain information on their current state which allows them to disregard conditions whose actuators are already triggered.

Infinite loops are often unavoidable for simple reflex agents operating in partially observable environments. Note: If the agent can randomize its actions, it may be possible to escape from infinite loops.

2.Model-based reflex agents

A model-based agent can handle a partially observable environment. Its current state is stored inside the agent maintaining some kind of structure which describes the part of the world which cannot be seen. This knowledge about "how the world works" is called a model of the world, hence the name "model-based agent".

A model-based reflex agent should maintain some sort of internal model that depends on the percept history and thereby reflects at least some of the unobserved aspects of the current state. It then chooses an action in the same way as the reflex agent.

3.Goal-based agents

Goal-based agents further expand on the capabilities of the model-based agents, by using "goal" information. Goal information describes situations that are desirable. This allows the agent a way to choose among multiple possibilities, selecting the one which reaches a goal state. Search and planning are the subfields of artificial intelligence devoted to finding action sequences that achieve the agent's goals.

In some instances the goal-based agent appears to be less efficient; it is more flexible because the knowledge that supports its decisions is represented explicitly and can be modified.

4.Utility-based agents

Goal-based agents only distinguish between goal states and non-goal states. It is possible to define a measure of how desirable a particular state is. This measure can be obtained through the use of a utility function which maps a state to a measure of the utility of the state. A more general performance measure should allow a comparison of different world states according to exactly how happy they would make the agent. The term utility, can be used to describe how "happy" the agent is.

A rational utility-based agent chooses the action that maximizes the expected utility of the action outcomes- that is, the agent expects to derive, on average, given the probabilities and utilities of each outcome. A utility-based agent has to model and keep track of its environment, tasks that have involved a great deal of research on perception, representation, reasoning, and learning.

5.Learning agents

Learning has an advantage that it allows the agents to initially operate in unknown environments and to become more competent than its initial knowledge alone might allow. The most important distinction is between the "learning element", which is responsible for making improvements, and the "performance element", which is responsible for selecting external actions.

The learning element uses feedback from the "critic" on how the agent is doing and determines how the performance element should be modified to do better in the future. The performance element is what we have previously considered to be the entire agent: it takes in percepts and decides on actions.

The last component of the learning agent is the "problem generator". It is responsible for suggesting actions that will lead to new and informative experiences.

- simple reflex agents
- goal-based agents
- utility-based agents
- agents that keep track of the world

Simple reflex Agents respond immediately to what it percepts. It will work only if the correct decision can be made on the basis of the current percept. Goal-based agents, as for the name itself goal-based agents acts so that they could achieve their goal. The utility-based agents uses a utility function which maps a state to a measure of the utility of the state. This type of agents also try to maximize their own "happiness". Agents that keep track of the world, complete state can be available if agent knows how environment evolves independently of agent, how agent's action effect the world. These type of agents are more powerfull than Simple Reflex Agents. It is used for more complex tasks.


http://www.cs.berkeley.edu/~russell/aima1e/chapter02.pdf
http://en.wikipedia.org/wiki/Intelligent_agent
http://amir.aavani.net/CS/AI-86/Chapter02.pdf.gz
http://books.google.com.ph/books?id=WldqydJ8JXQC&pg=PA54&lpg=PA54&dq=agents+that+keep+track+of+the+world&source=bl&ots=Wfov_9PPO7&sig=0_I7rDNdxeu-cnSrC6htXj3sBoY&hl=tl&ei=xWKLTatszfCtB83mncwO&sa=X&oi=book_result&ct=result&resnum=1&ved=0CBUQ6AEwADgK#v=onepage&q=agents%20that%20keep%20track%20of%20the%20world&f=false