Algorithmic learning theory. 4th international workshop on analogical and inductive inference, AII '94, 5th international workshop on algorithmic learning theory, ALT '94, Reinhardsbrunn Castle, Germany, October 10-15, 1994. Proceedings (Q1339499)

The articles of this volume will be reviewed individually. Indexed articles: \textit{Barzdinš, Jānis}, Towards efficient inductive synthesis from input/output examples, 1 [Zbl 1044.68638] \textit{Bibel, Wolfgang; Thielscher, Michael}, Deductive plan generation, 2-5 [Zbl 1044.68639] \textit{Dershowitz, Nachum}, From specifications to programs: Induction in the service of synthesis, 6-7 [Zbl 1044.68550] \textit{Zeugmann, Thomas}, Average case analysis of pattern language learning algorithms, 8-9 [Zbl 1044.68648] \textit{Ambainis, Andris; Smotrovs, Juris}, Enumerable classes of total recursive functions: Complexity of inductive inference, 10-25 [Zbl 1044.03528] \textit{Apsītis, Kalvis}, Derived sets and inductive inference, 26-39 [Zbl 1044.68635] \textit{Arnold, Oksana; Jantke, Klaus P.}, Therapy plan generation as program synthesis, 40-55 [Zbl 1044.68547] \textit{Bai, Shuo}, A calculus for logical clustering, 56-63 [Zbl 1044.68714] \textit{Baliga, Ganesh; Case, John}, Learning with higher order additional information, 64-75 [Zbl 1044.68637] \textit{Brāzma, Alvis; Čerāns, Kārlis}, Efficient learning of regular expressions from good examples, 76-90 [Zbl 1044.68640] \textit{Freivalds, Rūsiņš; Botuscharov, Ognian; Wiehagen, Rolf}, Identifying nearly minimal Gödel numbers from additional information, 91-99 [Zbl 1044.03529] \textit{Freivalds, Rūsiņš; Gobleja, Dace; Karpinski, Marek; Smith, Carl H.}, Co-learnability and FIN-identifiability of enumerable classes of total recursive functions, 100-105 [Zbl 1044.68641] \textit{Globig, Christoph; Lange, Steffen}, On case-based representability and learnability of languages, 106-120 [Zbl 1044.68654] \textit{Hirata, Kouichi}, Rule-generating abduction for recursive Prolog, 121-136 [Zbl 1044.68719] \textit{Iwatani, Toshiharu; Tano, Shun'ichi; Inoue, Atsushi; Okamoto, Wataru}, Fuzzy analogy based reasoning and classification of fuzzy analogies, 137-148 [Zbl 1044.68815] \textit{Koga, Yasuyuki; Hirowatari, Eiju; Arikawa, Setsuo}, Explanation-based reuse of Prolog programs, 149-160 [Zbl 1044.68722] \textit{Mofizur, Chowdhury Rahman; Numao, Masayuki}, Constructive induction for recursive programs, 161-175 [Zbl 1044.68725] \textit{Tu, Hsieh-Chang; Smith, Carl H.}, Training digraphs, 176-186 [Zbl 1044.68729] \textit{Abe, Naoki}, Towards realistic theories of learning, 187-209 [Zbl 1044.68634] \textit{Richert, Michael M.}, A unified approach to inductive logic and case-based reasoning (extended abstract), 210 [Zbl 1044.68727] \textit{Smith, Carl H.}, Three decades of team learning, 211-228 [Zbl 1044.68728] \textit{Auer, Peter; Cesa-Bianchi, Nicolò}, On-line learning with malicious noise and the closure algorithm, 229-247 [Zbl 1044.68636] \textit{Ben-David, Shai; Dichterman, Eli}, Learnability with restricted focus of attention guarantees noise-tolerance., 248-259 [Zbl 1044.68715] \textit{Brāzma, Alvis}, Efficient algorithm for learning simple regular expressions from noisy examples, 260-271 [Zbl 1044.68716] \textit{Chen, Zhixiang}, A note on learning DNF formulas using equivalence and incomplete membership queries, 272-281 [Zbl 1044.68718] \textit{Ferretti, Claudio; Mauri, Giancarlo}, Identifying regular languages over partially-commutative monoids, 282-289 [Zbl 1044.68653] \textit{Gasarch, William I.; Pleszkoch, Mark G.; Velauthapillai, Mahendran}, Classification using information, 290-300 [Zbl 1044.68642] \textit{Ishino, Akira; Yamamoto, Akihiro}, Learning from examples with typed equational programming, 301-316 [Zbl 1044.68720] \textit{Ishizaka, Hiroki; Arimura, Hiroki; Shinohara, Takeshi}, Finding tree patterns consistent with positive and negative examples using queries, 317-332 [Zbl 1044.68721] \textit{Jain, Sanjay}, Program synthesis in the presence of infinite number of inaccuracies, 333-348 [Zbl 1044.68551] \textit{Jain, Sanjay; Sharma, Arun}, On monotonic strategies for learning r.e. languages, 349-364 [Zbl 1044.68655] \textit{Kapur, Shyam}, Language learning under various types of constraint combinations, 365-378 [Zbl 1044.68656] \textit{Kimura, Shigetomo; Togashi, Atsushi; Shiratori, Norio}, Synthesis algorithm for recursive processes by \(\mu\)-calculus, 379-394 [Zbl 1044.68686] \textit{Kinber, Efim}, Monotonicity versus efficiency for learning languages from texts, 395-406 [Zbl 1044.68643] \textit{Kobayashi, Satoshi; Yokomori, Takashi}, Learning concatenations of locally testable languages from positive data, 407-422 [Zbl 1044.68658] \textit{Lange, Steffen; Nessel, Jochen; Wiehagen, Rolf}, Language learning from good examples, 423-437 [Zbl 1044.68659] \textit{Lange, Steffen; Watson, Phil}, Machine discovery in the presence of incomplete or ambiguous data, 438-452 [Zbl 1044.68723] \textit{Lange, Steffen; Zeugmann, Thomas}, Set-driven and rearrangement-independent learning of recursive languages, 453-468 [Zbl 1044.68644] \textit{Matsumoto, Satoshi; Shinohara, Ayumi}, Refutably probably approximately correct learning, 469-483 [Zbl 1044.68645] \textit{Mukouchi, Yasuhito}, Inductive inference of an approximate concept from positive data, 484-499 [Zbl 1044.68646] \textit{Nakamura, Atsuyoshi; Abe, Naoki; Takeuchi, Jun-ichi}, Efficient distribution-free population learning of simple concepts, 500-515 [Zbl 1044.68726] \textit{Okubo, Yoshiaki; Haraguchi, Makoto}, Constructing predicate mappings for goal-dependent abstraction, 516-531 [Zbl 1044.68732] \textit{Sakakibara, Yasubumi; Jantke, Klaus P.; Lange, Steffen}, Learning languages by collecting cases and tuning parameters, 537-546 [Zbl 1044.68661] \textit{Takimoto, Eiji; Tajika, Ichiro; Maruoka, Akira}, Mutual information gaining algorithm and its relation to PAC-learning algorithm, 547-559 [Zbl 1044.68647] \textit{Tanida, Noriyuki; Yokomori, Takashi}, Inductive inference of monogenic pure context-free languages, 560-573 [Zbl 1044.68662]