Advances in computer technology have pointed out the next important area of computer applications: solution of non-numerical problems. It is hardly necessary to emphasize the importance of these kind of problems. First of all most of the decisions one has to make in real-life situations are non-numerical in the first instance and can be represented as numerical problems only as approximations which are often only partially valid. Second, to use the computer to its full potential it should be employed as a logical machine, capable of deduction, and not just as a numerical calculating machine. Thus the computer would extend man's capability for logical reasoning and not just for his capability to do fast and accurate calculation. It is not a new area; indeed non-numerical problems are central in fields such as artificial intelligence, heuristic programming, pattern recognition, classification and information-processing (and retrival) etc. However, it is fair to assess that progress in the area has not been quite as expected. One of the reasons was a lack of conceptual and theoretical framework in which to investigate different classes of non-numerical problems to improve understanding of various types of problems and methods for their solutions and furthermore to enable the methods which have been proven as effective in one situation to be used in another situation with appropriately similar structure.

Advances in computer technology have pointed out the next important area of computer applications: solution of non-numerical problems. It is hardly necessary to emphasize the importance of these kind of problems. First of all most of the decisions one has to make in real-life situations are non-numerical in the first instance and can be represented as numerical problems only as approximations which are often only partially valid. Second, to use the computer to its full potential it should be employed as a logical machine, capable of deduction, and not just as a numerical calculating machine. Thus the computer would extend man's capability for logical reasoning and not just for his capability to do fast and accurate calculation. It is not a new area; indeed non-numerical problems are central in fields such as artificial intelligence, heuristic programming, pattern recognition, classification and information-processing (and retrival) etc. However, it is fair to assess that progress in the area has not been quite as expected. One of the reasons was a lack of conceptual and theoretical framework in which to investigate different classes of non-numerical problems to improve understanding of various types of problems and methods for their solutions and furthermore to enable the methods which have been proven as effective in one situation to be used in another situation with appropriately similar structure.

The present book is a festschrift in honor of Luigia Carlucci Aiello. The 18 articles included are written by former students, friends, and international colleagues, who have cooperated with Luigia Carlucci Aiello, scientifically or in AI boards or committees. The contributions by reputed researchers span a wide range of AI topics and reflect the breadth and depth of Aiello's own work.

Machine Models of Music brings together representative models and current research to illustrate the rich impact that artificial intelligence has had on the understanding and composition of traditional music and to demonstrate the ways in which music can push the boundaries of traditional Al research. Machine Models of Music brings together representative models ranging from Mozart's "Musical Dice Game" to a classic article by Marvin Minsky and current research to illustrate the rich impact that artificial intelligence has had on the understanding and composition of traditional music and to demonstrate the ways in which music can push the boundaries of traditional Al research.Major sections of the book take up pioneering research in generate-and-test composition (Lejaren Hiller, Barry Brooks, Jr., Stanley Gill); composition parsing (Allen Forte, Herbert Simon, Terry Winograd); heuristic composition (John Rothgeb, James Moorer, Steven Smoliar); generative grammars (Otto Laske, Gary Rader, Johan Sundberg, Fred Lerdahl); alternative theories (Marvin Minsky, James Meehan); composition tools (Charles Ames, Kemal Ebcioglu, David Cope, C. Fry); and new directions (David Levitt, Christopher Longuet-Higgins, Jamshed Bharucha, Stephan Schwanauer).Stephan Schwanauer is President of Mediasoft Corporation. David Levitt is the founder of HIP Software and head of audio products at VPL Research.

The following lecture notes were written shortly after I gave a course on capital theory in the winter-semester 1970/71 at the Univer sity of Heidelberg. While the general line of the argument is similar to the one in the course, I have modified and added a large number' of specific points in the process of writing the English version. I should like to emphasize the narrow limitations of the material covered in these notes. I have completely concentrated on steady states of stationary and exponentially growing economies, even up to the point where there is the danger of misleading the reader1 I have done this for several reasons. Other activities have not left me with a sufficient amount of time to be able to find the unifying principle of analysis and mode of presentation for the dynamic aspects of capi tal theory which would have made it worthwhile to add a sizeable book to the large body of literature in this field. On the other hand over the last couple of years I have become increasingly aware that some of the results in steady state capital theory (which could be derived without too much mathematical effort) are of relevance in present day dis cussions about the political role of economic theory and the relative merits of orthodox and radical economics. Also these results seemed not to be known by' mO$ of the participants in these discussions.