Term Projects:

Guidelines:
1. Projects can be done in groups of atmost two students.
2. The purpose of the project is to do research work on recent topics in AI.
Research publications are highly encouraged.
3. Groups should present their project proposals before the midsem. If you need to discuss with me for discussing your ideas, either mail me a draft of your idea or talk to me after the class.
4. Groups should email the group composition project title and an abstract to TA by September 15th.
5. It is desirable that the projects have a web page.
6. A report on the project should be submitted before the endsem.
Guidelines for preparing the report will be provided.
7. A final presentation on the project will be held before the endsem.
8. Projects will be evaluated based on, quality of work, novelty, presentation and report.
9. Plagiarism in any form will be strictly penalised.

List of Candidate Projects:

(Students may also propose projects not mentioned in this list.)



Emotion Recognition from Text and/or speech

Modeling Human Visual Attention in a Scene

Knowledge Management in Education/Health/Law

Named Entity Recognition in Text

Spock Challenge

Focussed Crawling


Drug Design
Title: Heuristic Search Algorithms for In-Silico Drug Design
Description:
Drug molecules are proteins which act by binding with some other proteins causing the disease. Binding (or docking) is determined by the 3-dimensional structure of the protein. Drug design in computers (in silico) involve finding out structures which can bind with a disease causing molecule. This can be posed as a search problem.
The problems bears similarity with robot path planning.

Goal of the project:
1. Formulate drug design as a search/CSP problem.
2. Design good heuristic functions.
3. Design efficient search algorithm.
4. Run the search algorithm to design drugs which can bind to some benchmark molecule.

References:
Search techniques for rational drug design, Finn, Kavraki, Latombe, Motwani, and Venkarasubramaniam.

RAPID: Randomized Pharmacophore Identification for Drug Design, Kavraki, Latombe et al

QA
Title: Question Answering in Restricted Domains
Description:
Question answering was one of the earliest AI tasks considered. Although, no general purpose QA system exist till date. QA systems for restricted domains like law, medical system achieved considerable success. Components of a QA system includes, representing and building a knowledge base, logical inferencing and natural language interface.

Goal of the project:
1. To build a knowledge base for a restricted domain, (say, academic rules at IIT Kharagpur, Cricket World Cup, railway travel etc) in a suitable framework (e.g., XML). The knowedge may be extracted from web pages containing institute academic rules/cricket.org.
2. Build an inference engine.
3. Build an user interface.
4. Integrate the systems to build a Web-based Virtual Faculty Advisor/Cricket expert etc

References:
ACL 2004 Workshop on Question Answering in Restricted Domain

Text REtrieval Conference (TREC) QA Data

Is Question Answering an Acquired Skill?

GGP
Title: General Game Playing
Description:
General game players are computer systems able to accept formal descriptions of arbitrary games and able to play those games effectively without human intervention. General game playing systems are characterized by their use of general cognitive information-processing technologies (such as knowledge representation, reasoning, learning, and rational behavior). Unlike specialized game playing systems (such as Deep Blue), they do not rely on algorithms designed in advance for specific games.

Goal of the Project:
1. To particpate in the AAAI 2005 GGP Competition

Reference:
http://www.aaai.org/Conferences/National/2005/games05.html

TA
Title: Automated Trading Agent
Description:
Building an agent for automated trading in a stock market is a challenging AI task. It is an optimization problem in a multiagent dynamic environment. Currently popular approaches involve using game theory and mechanism design techniques.

Goal of the Project:
1. To design an agent to operate in the Penn-Lehmann ATA  environment.

References:
1. http://www.cis.upenn.edu/~mkearns/projects/plat.html

2. The Penn-Lehman Automated Trading Project.
Michael Kearns and Luis Ortiz. IEEE Intelligent Systems, 2003.
[PDF]


Mozilla Autocomplete
Title: Machine Learning for Mozilla Autocomplete
Description:
Most browsers support autocomplete features. Mozilla has been fostering an open source project on using machine learning algorithms for enhanced autocompletion. A beta version is available with the latest Firefox release. Mozilla also plans to use machine learning in other aspects of the browser.

Goal of the Project:
1. To build a Mozilla plugin which uses machine learning for autocomplete.

Reference:
http://www.mozilla.org/projects/ml/autocomplete/


KDD Cup
Title: KDD Cup Query Categorization Task
Description:
The knowledge discovery cup is a competition  held every year on data mining problems. This years task involves categorizing search engine queries.  The
task is to categorize 800,000 queries into 67 predefined categories. The meaning and intention of search queries is subjective. A search query "Saturn" might mean Saturn car to some people and Saturn the planet to others. We will use multiple human editors to classify a subset of queries selected from the total set given to you. The collection of human editors is assumed to have the most complete knowledge about internet as compared with any individual end user. A portion of the editor labeled queries is given to you (CategorizedQuerySample.txt in the zip file for downloading) and the rest will be held back for evaluation. You will not know which queries will be used for evaluation and are asked to categorize all queries given.

Goal of the Project:
1. To build a query categorizer.

Reference:
http://kdd05.lac.uic.edu/kddcup.html

Computer Vision
Title: Computer Vision: Where am I?
Description:

Contestants are given a collection of color images taken by a calibrated digital camera.  The photographs have been taken at various locations and often share overlapping fields of view or certain objects in common.  The GPS locations for a subset of the images are provided.  The goal of the contest is to guess, as accurately as possible, the GPS locations of the un-labeled images.

Contestants are free to use whatever combination of programs they wish, including existing imaging libraries.  The compiled executable must read a descriptor file that contains a list of images and associated GPS locations (for a subset of the images), as well as open and process the JPEG images listed in this file.  Its output must be a similar descriptor file, with the missing GPS locations filled in, which is then sent to the evaluation system for scoring.

The system may also be used for locating players/balls in sports events.

Goal of the Project:
1. To design and implement an algorithm for the above task.

Reference:
http://research.microsoft.com/iccv2005/Contest/


ADG
Title: Automatic Deduction in Geometry
Description:
Automated theorem proving has been widely studied in AI literature. It is interesting to study automated reasoning/deduction systems for proving geometry theorems expressed in diagramatic form.

Goal of the Project:
1. Develop a suitable representation of (plane) geometry theorems.
2. Develop a theorem proving system (Prolog or other inference/planning engines may be used)

References:
http://www.risc.uni-linz.ac.at/about/conferences/adg2002/


Preference Elictitation
Title: Preference Elicitation System
Description:
Preference elicitation systems are a part of decision support systems which collect information about user's preferences and guides the user towards a desired goal. Examples include a internet shopping assistant (a primitive version of which is provided by Amazon).

Goal of the Project:
Use AI techniques to develop a preference elictitation  system for a book shop.

References:
Survey of Preference Elicitation Methods

A POMDP Formulation of Preference Elicitation Problems


Cognition, AI and Creativity, AI in Music, AI in Game Playing