CPE/CSC 480-F08 Artificial Intelligence Fall 2008 Project Topics

Collaboration with Others

Ontology Learning (CAD-RC)

In connection with computers, ontologies are used to capture specifications of entities and their relationships, often in order formalize the concepts and terms used in a domain. Since the development of such ontologies can be very time-consuming and tedious, research is being done into learning ontologies from sources like document collections, data sets, or software specifications and implementations.

The Collaborative Agent Design Research Center (CAD-RC) at Cal Poly is exploring the use of ontology learning methods for some of their projects in areas like logistics and the categorization of Improvised Explosive Devices (IEDs). The goal of this project is to explore and evaluate methods and tools for ontology learning.

Sensemaking

In complex and evolving situations, it can become difficult for individuals, groups or organizations to understand "what is going on", and to form mental models for the situation that may allow decisions about possible actions and their potential outcomes.

The goal of this project is an exploration of computer-supported methods for sensemaking, especially in the context of large organizations and business decision making.

I'm in contact with a large software organization, and there may be opportunities for on-going research projects.

Personal Knowledge Organizer

While search engines and the use of related technologies on personal computers (such as Apple's Spotlight, or Google Desktop Search) allow users to find information based on keywords relatively easily, the task of organizing knowledge contained in documents still relies heavily on work done by humans. People frequently use ad-hoc solutions like directories and symbolic links for this purpose, resulting in very brittle and individualistic solutions.

The goal of this project is to examine methods for the automatic categorization and labeling of documents in combination with personalized ontologies in order to create a system that enables a more systematic and less tedious way of organizing documents based on their contents.

Netflix Prize

While Netflix has one of the most prominent recommendation systems for the selection of movies, many of you have probably had experiences with less than convincing suggestions for movies. Such recommendation systems take the preferences of many users, and try to predict what movies a particular user might like based on what the merchant knows about the user. Netflix has an ongoing contest to improve the accuracy of predictions: If you can beat their system, Cinematch, by 10%, you can win one million dollars. As if Sep. 22, the current leader has a 9.15% improvement over Cinematch. Progress has not been very swift, last year's "progress prize" was awarded for a score of 8.43%; check the leaderboard for the latest results.

Paypal Programming Contest

PayPal has a programming contest, the>Paypal College Challenge. In this contest, student teams have the opportunity to compete for an opportunity to present their product at PayPal headquarters in San Jose, and to receive summer internships with PayPal. There is no specific mention of AI in the contest, but if you're interested in doing a project that combines AI and PayPal's eCommerce and ePayment systems, let me know. The deadline to register for this contest is September 30, and the deadline to submit the final demo is November 21. For more details, see the PayPal College Challenge Website.

• Prof. David Gillette from the English department has an ongoing project, Lumiere Ghosting . The goal of this project is to allow people to immerse themselves into a virtual environment via avatars created from their picture. He is interested in collaboration with CSc students, e.g. to endow the avatars with some rudimentary intelligence, or to add computer-based agents into the environment.
• Dr. Kevin O'Gorman (the game of Hex)

Further Project Ideas

• Games: Computer games are concentrating more and more on AI techniques, e.g. to generate more credible agents. Of course many of you know much more about such games than I do ;-)
• AI and Music: AI techniques have been used for various purposes, such as the generation of music in a particular style (e.g. Bach), suggesting songs based on user preferences (Pandora.com, Apple's "Genius" addition to iTunes), accompanying players, auto-tuning guitars (e.g. Gibson's Robo-Guitar), and probably many more. If you play the guitar and are interested in this, there may be opportunities to do something in collaboration with the local guitar manufacturer Ernie Ball (MusicMan).
• Artificial Life: The simulation of "creatures" (e.g. walkers, crawlers, worms) or populations (swarms of birds, ant hills, bee hives) frequently based on evolutionary or genetic algorithms.
• Genetic Algorithms: Conceptually a variation of search algorithms, these techniques are useful for certain optimization tasks, based on the "survival of the fittest" principle People have applied it to VLSI layout, scheduling, code generation, design optimization, and other, more exotic tasks.
• Neural Networks: These collections of simple computational elements are one of the frequently used learning methods in AI. They are used for tasks requiring classification or categorization, generalization from examples, or identification of similarities.
• Fuzzy Logic: In contrast to standard logic with its binary values, fuzzy logic employs linguistic variables such as "very tall" to capture the essential aspects. This makes many tasks such as process control much easier, but requires different reasoning methods.
• Robotics: The use of physical agents often illustrates the difference between easily solvable toy problems and their counterparts in the real world. I have one simple "Lego robot" that can be programmed in Java or a C-like language.
• Natural Language Processing: With building blocks such as Link Grammar or WordNet, it is possible to construct systems that extract meaningful information from text-based documents.
• Semantic Web: The Semantic Web adds additional information to Web pages in the form of meta-tags. This enables computers to perform more meaningful operations on Web pages. For example, it allows the search for concepts based on ontologies, rather than simple keywords.