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#import "MyPickerDelegateh" @implementation MyPickerDelegate @synthesize myData; @synthesize myData2; - (id) init { if([super init] == nil) return nil; myData = [[NSArray alloc]initWithObjects: @"Red", @"Yellow", @"Green", @"Blue", @"Purple", @"Orange", @"Black", @"Gray", @"Tan", @"Pink", @"Coral", nil]; myData2 = [[NSArray alloc] initWithObjects: @"Very Dark", @"Dark", @"Normal", @"Light", @"Very Light", nil]; return self; } - (NSInteger) numberOfComponentsInPickerView: (UIPickerView *) pickerView { return 2; } - (void)pickerView:(UIPickerView *)pickerView didSelectRow:(NSInteger)row inComponent:(NSInteger)component { NSLog(@"picked row: %i, component: %i", row, component); if(component == COLOR_WHEEL) NSLog(@"the value: %@", [selfmyData objectAtIndex:row]); else NSLog(@"the value: %@", [selfmyData2 objectAtIndex:row]); } - (NSInteger) pickerView: (UIPickerView *) pickerView numberOfRowsInComponent: (NSInteger) component { if(component == COLOR_WHEEL) return [selfmyData count]; else return [selfmyData2 count]; } - (NSString *) pickerView: (UIPickerView *) pickerView titleForRow: (NSInteger) row forComponent: (NSInteger) component { if(component == COLOR_WHEEL) return [selfmyData objectAtIndex:row]; else return [selfmyData2 objectAtIndex:row]; } - (void)dealloc { [myData release]; [myData2 release]; [super dealloc]; } @end

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Note these two rules compactly describe a recursive function that would be very difficult to represent using a decision tree or other propositional representation One way to see the representational power of first-order rules is to consider the In programs are sets of general purpose programming language PROLOG PROLOG, first-order rules such as the two shown above (rules of this form are also called H r clauses) In fact, when stated in a slightly different syntax the above rules on form a valid PROLOG program for computing the Ancestor relation In this light, a general purpose algorithm capable of learning such rule sets may be viewed as an algorithm for automatically inferring PROLOG programs from examples In this chapter we explore learning algorithms capable of learning such rules, given appropriate sets of training examples In practice, learning systems based on first-order representations have been successfully applied to problems such as learning which chemical bonds fragment in a mass spectrometer (Buchanan 1976; Lindsay 1980), learning which chemical substructures produce mutagenic activity (a property related to carcinogenicity) (Srinivasan et al 1994), and learning to design finite element meshes to analyze stresses in physical structures (Dolsak and Muggleton 1992) In each of these applications, the hypotheses that must be represented involve relational assertions that can be conveniently expressed using first-order representations, while they are very difficult to describe using propositional representations In this chapter we begin by considering algorithms that learn sets of propositional rules; that is, rules without variables Algorithms for searching the hypothesis space to learn disjunctive sets of rules are most easily understood in this setting We then consider extensions of these algorithms to learn first-order rules Two general approaches to inductive logic programming are then considered, and the fundamental relationship between inductive and deductive inference is explored

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Sides and Type DVD-ROM (DVD-5) DVD-ROM (DVD-9) Size 12 cm 12 cm Layersa 1 side (1 layer) 1 side (2 layers) 2 sides (1 layer each) 2 sides (1 and 2 layers) 2 sides (2 layers each) 1 side (1 layer) 1 side (2 layers) 2 sides (1 layer each) 2 sides (1 and 2 layers) 2 sides (2 layers each) 1 side 1 side 2 sides 1 side 1 side 2 sides 1 side 2 sides 1 side 2 sides 1 side 2 sides 1 side 2 sides 1 side 1 side Billions of Bytesb 470 854 940 1324 1708 146 265 292 412 531 395 470 940 470 258 516 470 940 146 292 470 940 470 940 068 136

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Here we consider a family of algorithms for learning rule sets based on the strategy of learning one rule, removing the data it covers, then iterating this process Such algorithms are called sequential covering algorithms To elaborate, imagine we that have a subroutine LEARN-ONE-RULE accepts a set of positive and negative training examples as input, then outputs a single rule that covers many of the

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Approx Gigabytesb 437 795 875 1233 1591 136 247 272 383 495 367 437 875 437 240 480 437 875 136 272 437 875 437 875 064 128 Playing Timec 225 h 4h 45 h 625 h 8h 075 h 125 h 15 h 2h 25 h 175 h 225 h 45 h 225 h 125 h 25 h 225 h 45 h 075 h 15 h 225 h 45 h 225 h 45 h 025 hd 05 hd

- (IBAction) changeColor: (id) sender { NSLog(@"the color is: %@ and the shade is: %@", (NSString *) [myPickerDelegatemyData objectAtIndex: [myPicker selectedRowInComponent: COLOR_WHEEL]], (NSString *)[myPickerDelegate myData2 objectAtIndex: [myPicker selectedRowInComponent:SHADE_ WHEEL]]); }

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