Starting with 100 cases, 10 outcomes, 15 variables
Form 100 rules, each with 15 antecedents and one consequent.
Cancellations: If we have
C, A => B and –C, A => B, collapse to A => B
D, E => F and D, G => F, collapse to D => F
Test rules and undo collapse if performance gets worse
Additional heuristics for combining rules.
Rose Diagnosis R1: If not yellow leaves and wilted leaves and brown spots then fungus. … R6: If wilted leaves and yellow leaves and not brown spots then bugs N Y Y Bugs Y N Y Fungus Y N N Fungus N N Y Nutrition Y Y N Bugs Y Y N Fungus Brown Spots Wilted Leaves Yellow Leaves
Develop a decision tree to model the decision a patron makes when deciding whether or not to wait for a table at a restaurant
Two classes: wait, leave
Ten attributes: Alternative available? Bar in restaurant? Is it Friday? Are we hungry? How full is the restaurant? How expensive? Is it raining? Do we have a reservation? What type of restaurant is it? What’s the purported waiting time?
Human-aided trees like Animals are also generally clear and meaningful, could easily be modified by humans
Inferred rules like ID3's are generally understood by humans but may not be intuitively obvious. Modifying them by hand may lead to worse results.
Systems like neural nets are typically black box: you can look at the functions and weights but it's hard to interpret them in any human-meaningful way and essentially impossible to modify them by hand.
Learning Applied to Ground Robots (LAGR) . The goal of the LAGR program is to develop a new generation of learned perception and control algorithms for autonomous ground vehicles, and to integrate these learned algorithms with a highly capable robotic ground vehicle.
Personalized Assistant that Learns (PAL) The mission of the PAL program is to radically improve the way computers support humans by enabling systems that are cognitive, i.e., computer systems that can reason, learn from experience, be told what to do, explain what they are doing, reflect on their experience, and respond robustly to surprise .
Transfer Learning : The goal of the Transfer Learning Program is to develop, implement, demonstrate and evaluate theories, architectures, algorithms, methods, and techniques that enable computers to apply knowledge learned for a particular, original set of tasks to achieve superior performance on new, previously unseen tasks .