Algorithmic Learning Theory, Vol. 997 Book

	Towards Efficient Inductive Synthesis from Input/Output Examples	1
	Deductive Plan Generation	2
	From Specifications to Programs: Induction in the Service of Synthesis	6
	Average Case Analysis of Pattern Language Learning Algorithms	8
	Enumerable Classes of Total Recursive Functions: Complexity of Inductive Inference	10
	Derived Sets and Inductive Inference	26
	Therapy Plan Generation as Program Synthesis	40
	A Calculus for Logical Clustering	56
	Learning with Higher Order Additional Information	64
	Efficient Learning of Regular Expressions from Good Examples	76
	Identifying Nearly Minimal Godel Numbers from Additional Information	91
	Co-Learnability and FIN-Identifiability of Enumerable Classes of Total Recursive Functions	100
	On Case-Based Representability and Learnability of Languages	106
	Rule-Generating Abduction for Recursive Prolog	121
	Fuzzy Analogy Based Reasoning and Classification of Fuzzy Analogies	137
	Explanation-Based Reuse of Prolog Programs	149
	Constructive Induction for Recursive Programs	161
	Training Digraphs	176
	Towards Realistic Theories of Learning	187
	A Unified Approach to Inductive Logic and Case-Based Reasoning	210
	Three Decades of Team Learning	211
	On-line Learning with Malicious Noise and the Closure Algorithm	229
	Learnability with Restricted Focus of Attention Guarantees Noise-Tolerance	248
	Efficient Algorithm for Learning Simple Regular Expressions from Noisy Examples	260
	A Note on Learning DNF Formulas Using Equivalence and Incomplete Membership Queries	272
	Identifying Regular Languages over Partially-Commutative Monoids	282
	Classification Using Information	290
	Learning from Examples with Typed Equational Programming	301
	Finding Tree Patterns Consistent with Positive and Negative Examples Using Queries	317
	Program Synthesis in the Presence of Infinite Number of Inaccuracies	333
	On Monotonic Strategies for Learning r.e. Languages	349
	Language Learning under Various Types of Constraint Combinations	365
	Synthesis Algorithm for Recursive Processes by [mu]-Calculus	379
	Monotonicity versus Efficiency for Learning Languages from Texts	395
	Learning Concatenations of Locally Testable Languages from Positive Data	407
	Language Learning from Good Examples	423
	Machine Discovery in the Presence of Incomplete or Ambiguous Data	438
	Set-Driven and Rearrangement-Independent Learning of Recursive Languages	453
	Refutably Probably Approximately Correct Learning	469
	Inductive Inference of an Approximate Concept from Positive Data	484
	Efficient Distribution-Free Population Learning of Simple Concepts	500
	Constructing Predicate Mappings for Goal-Dependent Abstraction	516
	Learning Languages by Collecting Cases and Tuning Parameters	532
	Mutual Information Gaining Algorithm and Its Relation to PAC-Learning Algorithm	547
	Inductive Inference of Monogenic Pure Context-Free Languages	560
	Index of Authors	575