Logical Foundations of Artificial Intelligence

Michael R. Genesereth

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Logical Foundations of Artificial Intelligence is an account of the fundamentals of artificial intelligence from the standpoint of logic.

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I wanted to ask Stack Overflow users for a nice idea for a project that could entertain a fellow student programmer during a semester. Computer vision might look interesting, although I couldn't say if a project on that field is something that could be achievable in 4 months. What do you think?

Can't tell without knowing more about you, your friend, and the project. My guess is "no".

I'd point you towards two sources. The first is Peter Norvig's "Artificial Intelligence"; the second is "Programming Collective Intelligence". Maybe they'll inspire you.

There is a story that, during the early days of AI research when significant progress was being made on "hard" logic problems via mechanical theorem provers, a professor assigned one of his graduate students the "easy" problem of solving how vision provided meaningful input to the brain. Obviously, things turned out to be far more difficult than the professor anticipated. So, no, not vision in the general sense.

If you are just starting in AI, there are a couple of directions. The classic AI problems - logic puzzles - are solved with a mechanical theorem prover (usually written in Lisp - see here for the classic text on solving logical puzzles). If you don't want to create your own, you can pick up a copy of Prolog (it is essentially the same thing).

You can also go with pattern recognition problems although you'll want to keep the initial problems pretty simple to avoid getting swamped in detail. My dissertation involved the use of stochastic proccesses for letter recognition in free-floating space so I'm kind of partial to this approach (don't start with stochastic processes though, unless you really like math). Right next door is the subfield of neural networks. This is popular because you almost can't learn NN without building some interesting projects. Across this entire domain (pattern processing), the cool thing is that you can solve real problems rather than toy puzzles.

A lot of people like Natural Language Processing as it is easy to get started but almost infinite in complexity. One very definable problem is to build an NLP program for processing language in a specific domain (discussing a chess game, for example). This makes it easy to see progress while still being complex enough to fill a semester.

Hope that gives you some ideas!

I've been looking at the freebase project for storing data. It seems to be a great place to store concrete, objective data like names, locations and dates. Is it a good place to store subjective data like opinions or ratings? Is there another/better open data, semantic data store or strategy for storing and querying this kind of information?

Additionally, since it is subjective I can be sure that others will not agree with my opinion. How would I store the opinions of others inline so the crowd opinion could be represented better?

Is freebase the right place to store this type of data?

For example: a restaurant rating or a movie rating. The movie rating would probably be less time sensitive than the restaurant rating. Any non-identifying information about the person who entered the data would be interesting for determining other factors and relationships.

The Semantic Web is more or less a variant of first-order logic, for the most part, so the important part is to have a clear understanding of what each of your predicates "mean". This idea is very simple but applicable to a wide-variety of meaning representations - i.e. it is behind the entity model of databases.

There should be no problem representing the information you mentioned in a semantic web representation. Just be sure to have a clear definition of what each of your predicates denote, so that the meaning doesn't shift over time and you end up with an inconsistent representation.

Genesereth's book is old but a good one if you are interested in reading about this in further detail. I think a lot of people who worked on the Semantic Web were involved in Douglas Lenat's Cyc project which gradually shifted to a logic-based meaning representation over time.


The site for Cyc: