intelligent agents kriti puniyani – 04305012 neela sawant – 04305811 under the guidance of prof....
TRANSCRIPT
Intelligent Agents
Kriti Puniyani – 04305012Neela Sawant – 04305811
Under the Guidance of Prof. Pushpak Bhattacharyya
Agenda
What is an agent ? Structure of Intelligent Agents Types of Agents Need for Learning Syskill and Webert – An Intelligent Agent
What is an agent ?
An agent is anything that perceives its environment through sensors and acts upon that environment through actuators.
PESA concepts :Percepts, Environment, Sensors, Actuators
Some Examples
robot Camera, sound recorder , etc. for sensors Robotic arms, wheels, speakers, etc. for
actuators software agent (softbot)
functions as sensors information provided as input to functions in
the form of encoded bit strings or symbols functions as actuators
results deliver the output
How is an “agent” different from a traditional program?
Autonomy Personalizability Pro-activity Cooperation-interactivity
Structure of Agents Agent = architecture + program Agent program: the implementation of agent's perception-action mapping Architecture: a device that can execute the agent program (e.g., general-purpose computer, specialized device, robot)
Types of Agents Simple reflexive agents Model-based reflex agents (agents with internal
states) Goal Based Agents Utility Based Agents All can be turned into learning agents.
Simple reflexive agentsAct on current percept, ignoring percept
history.
Model-based agentsUse internal states (or models) to deal with
the world that is only partially observable.
Goal-based agents
Anticipating future … involving planning and search. More flexible behavior.
Utility Based AgentsImprove upon the goal-based agents by
having high-quality behavior in most environment.
Learning Agents
Four main components: learning element, performance element, critic and problem generator.
Syskill & Webert
An Intelligent Agent That Identifies Interesting Websites
Motivation Information Overload on the Internet. It has been estimated that only 26% of Lycos
results are actually interesting. Syskill & Webert is an intelligent agent that
learns the profile of the user, and uses it to suggest interesting pages to the user.
Functionality
Learns a profile for every topic of interest of the user.
Uses an index page or Lycos to find other web-pages on the topic of interest, and rate them according to user profile.
Allows the user to give an initial set of good or bad web-pages.
Rating Of Pages
Suggested Web Sites
Overall Block Diagram Of the System
How is the classification done? Naive Bayes Classifier TF-IDF & Clustering Algorithms Neural Networks Self Organising Maps
Is Bayes Theorem Applicable?
Bayes Theorem : P(Y|X) = P(X|Y) * P(Y) / P(X)
Y= Website Intersting (+) or not (-) X = Vector of words Question : Is it simpler to calculate
P(+|X) or P(X|+) Calculate P(+|X) - data sparsity
problem
Naïve Bayes Classification Web page to be classified is converted into a
Boolean feature vector – X. P(+ | X) = P(X | +). P(+) (P(Denom)
ignored)= P(X1, X2, X3, ....Xk | +). P(+)=P(X1|+). P(X2|+). P(X3|
+). .....P(Xk|+).P(+)(Naive Bayes Assumption)
Similarly, P(- | X) = P(X1|-). P(X2|-). P(X3|-). .....P(Xk|-).
P(-) If P(+ | X) > P(- | X) significantly, then X is an
interesting web page.
Construction of Feature Vectors
User rates html pages as either hot or cold, which gives us the set of positive and negative examples.
Each example is analysed to find set of k most informative words, and a Boolean feature vector is constructed, in which 1 indicates the presence of word i in the document and 0 indicates absence of the word.
Words from stop list are not considered.
Example Interesting Web Pages:
<title>Supervised Learning requires a set of positive and negative examples.</title>
<body>Rote learning is not a very efficient learning technique.
Uninteresting Web Pages: Does there exist an efficient method of
learning? 4 most informative words:
learning, supervised, rote, technique
Probability Calculation P(learning | +)=3/22 P(supervised |
+)=1/22P(rote | +)=1/22 P(technique | +)=1/22
P(learning | -)=1 / 8 P(supervised | -)=0P(rote | -)=0 P(technique | -)=0
P(+) = 2 / 3 P(-) = 1 / 3 Due to presence of 0 probabilities, we need
to smoothen the data.
Smoothing Add 1 to the Numerator & k to the
Denominator for every conditional probability calculated.
P(learning | +)=4/26 P(supervised | +)=2/26P(rote | +)=2/26 P(technique | +)=2/26
P(learning | -)=2/12 P(supervised | -)=1/12P(rote | -)=1/12 P(technique | -)=1/12
P(+) = 2/3 P(-) = 1/3
Classification
Incoming Web Site:<u>I Like Machine Learning
Techniques</u> X = < l, s, r, t> X = <1, 0, 0, 1> P(+ | X) = 4/26 * 2/26 * 2/3 =
0.0079 P(- | X) = 2/12 * 1/12 * 1/3 =
0.0046 P(+ | X) > P(- | X) => Interesting
Web Site
Till Now... We discussed how the user rates sites
as hot or cold. Analysed them, & extracted most
informative features Got other html pages from the net,
classified them, and presented them to the user
Issues: Where is the learning of the profile? Can we make this work without the
initial set of pages?
Learning of Profile
User can also rate pages suggested by the agent, and the agent then re-calculates the k-most informative words and their probabilities – thus learning takes place.
k-most informative words & their probabilities are stored as user profile for the particular topic.
User can specify keywords (and optionally probabilities ) instead of rating pages as initial input.
Advantages & Disadvantages Simple, efficient, good performance Even if only 20 pages is required as
training data, expecting a user to rate 20 web-pages before he can see results is not a very good idea.
Every html page has to be prefetched before it can be rated.
Choice of k-most informative words determines the effectiveness of the results observed.
Current Status & Future Work User does not need to specify the
topic of interest – the agent is able to learn new interest topics by keeping track of web sites being accessed, and even scanning mail.
It is also able to collaborate with agents of other users to be able to recommend sites, movies etc. - although not very efficiently as of now.
Current work going on is to maintain a common profile across mutiple agents of the same user.
Conclusion
Agents encompass almost all fields of AI from knowledge bases to learning, and are one of the most widely researched areas in core AI today.
Syskill and Webert is the “classic personal agent”, which though not practically very efficient, brings out the basic issues in agent design, in a comprehensible and logical manner.
References
Russel, S. J., and Norvig P. (1995). Artificial Intelligence. Prentice Hall, New Jersey.
http://more.btexact.com/projects/agents.htm
Syskill & Webert : Identifying Interesting Web sites – Michael Pazzani, Jack Muramatsu, & Daniel Billsus (1998)
Living with Agents : Jaron Collis, Stuart Soltysiak, Divine Ndumu and Nader Azarmi - BT Technology Journal, 18(1), pp. 66-7, 2000.
Agents of Change : Patrick Thibodeau - SEPTEMBER 06, 2004 (COMPUTERWORLD)
Knowing Me, Knowing You – Practical Issues in the personalistaion of Agent Technology : Stuart Soltysiak and Barry Crabtree(2000)
Thank You
k-most Informative Words Words that appear frequently in the hot
list, and not in the cold list, or vice versa.
Expected GainE(W, S)=I(S) - [P(W= present)* I(S|
W=present)+ P(W = absent)* I | (S | W =
absent) Information Content
I(S) = - P(Sc) * log P(Sc)c { hot, cold }