[net.ai] Learning Bibliography

Laws@SRI-AI.ARPA@sri-unix.UUCP (08/09/83)

From:  Ken Laws <Laws@SRI-AI.ARPA>

Anderson, J.R. Farrell, R. Sauers, R.* Learning to plan in LISP.* 
Carnegie Mellon U. Psych.Dept.*1982.

Barber, G.*Supporting organizational problem solving with a 
workstation.* M.I.T. A.I. Lab.*Memo 681.*1982.

Bundy, A. Silver, B.*A critical survey of rule learning programs.* 
Edinburgh U. A.I. Dept.*Res. Paper 169.*1982.

Carbonell, J.G.* Learning by analogy: formulating and generalizing 
plans from past experience.* Carnegie Mellon U.  
Comp.Sci.Dept.*CMU-CS-82-126.*1982.

Carroll, J.M. Mack, R.L.* Metaphor, computing systems, and active 
learning.* IBM Watson Res. Center.*RC 9636.*1982.  schemes.* IBM 
Watson Res. Center.*RJ 3645.*1982.

Cohen, P.R.* Planning and problem solving.* Stanford U.  
Comp.Sci.Dept.*STAN-CS-82-939; Stanford U. Comp.Sci.Dept.  Heuristic 
Programming Project.*HPP-82-021.*1982.  61p.

Dellarosa, D. Bourne, L.E. Jr.*Text-based decisions: changes in the 
availability of facts due to instructions and the passage of time.* 
Colorado U. Cognitive Sci.Inst.* Tech.rpt. 115-ONR.*1982.

Ehrlich, K. Soloway, E.*An empirical investigation of the tacit plan 
knowledge in programming.* Yale U.  Comp.Sci.Dept.*Res.Rpt.  
236.*1982.

Findler, N.V. Cromp, R.F.*An artificial intelligence technique to 
generate self-optimizing experimental designs.* Arizona State U.  
Comp.Sci.Dept.*TR-83-001.* 1983.

Good, D.I.* Reusable problem domain theories.* Texas U.  Computing 
Sci.Inst.*TR-031.*1982.

Good, D.I.* Reusable problem domain theories.* Texas U.  Computing 
Sci.Inst.*TR-031.*1982.

Kautz, H.A.*A first-order dynamic logic for planning.* Toronto U.  
Comp. Systems Res. Group.*CSRG-144.*1982.

Luger, G.F.*Some artificial intelligence techniques for describing 
problem solving behaviour.* Edinburgh U. A.I.  Dept.*Occasional Paper 
007.*1977.

Mitchell, T.M. Utgoff, P.E. Banerji, R.* Learning by experimentation:
acquiring and modifying problem solving heuristics.* Rutgers U.  
Comp.Sci.Res.Lab.*LCSR-TR-31.* 1982.

Moura, C.M.O. Casanova, M.A.* Design by example (preliminary report).*
Pontificia U., Rio de Janeiro.  Info.Dept.*No. 05/82.*1982.

Nadas, A.*A decision theoretic formulation of a training problem in 
speech recognition and a comparison of training by uncondition versus 
conditional maximum likelihood.* IBM Watson Res. Center.*RC 
9617.*1982.

Slotnick, D.L.* Time constrained computation.* Illinois U.  
Comp.Sci.Dept.*UIUCDCS-R-82-1090.*1982.

Tomita, M.* Learning of construction of finite automata from examples 
using hill climbing.  RR: regular set recognizer.* Carnegie Mellon U.
Comp.Sci.Dept.* CMU-CS-82-127.*1982.

Utgoff, P.E.*Acquisition of appropriate bias for inductive concept 
learning.* Rutgers U. Comp.Sci.Res.Lab.* LCSR-TM-02.*1982.

Winston, P.H. Binford, T.O. Katz, B. Lowry, M.* Learning physical 
descriptions from functional definitions, examples, and precedents.* 
M.I.T. A.I. Lab.*Memo 679.* 1982.

Winston, P.H.* Learning by augmenting rules and accumulating censors.*
M.I.T. A.I. Lab.*Memo 678.*1982.