Jean-Pierre Briot

Lars Braubach

John Thangarajah

The earliest work on agents may be traced at least to the ?rst conceptualization of the actor model by Carl Hewitt. In a paper in an AI conference in the early 1970s, Hewitt described actors as entities with knowledge and goals. Research on actors continued to focus on AI with the development of the Sprites model in which a monotonically growing knowledge base could be accessed by actors (inspired by what Hewitt called “the Scienti?c Computing Metaphor”). In the late1970sandwellinto 1980s,controversyragedinAIbetweenthosearguingfor declarative languages and those arguing for procedural ones. Actor researchers stood on the side of a procedural view of knowledge, arguing for an open s- tems perspective rather than the closed world hypothesis necessary for a logical, declarativeview. In the open systemsview,agentshad armslength relationships and could not be expected to store consistent facts, nor could the information in a system be considered complete (the “negation as failure” model). Subsequent work on actors, including my own, focused on using actors for general purpose concurrent and distributed programming. In the late 1980s, a number of actor languages and frameworks were built. These included Act++ (in C++) by Dennis Kafura and Actalk (in Smalltalk) by Jean-Pierre Briot. In recent times, the use of the Actor model, in various guises, has proliferated as new parallel and distributed computing platforms and applications have become common:clusters,Webservices,P2Pnetworks,clientprogrammingonmulticore processors, and cloud computing.

7th International Workshop, ProMAS 2009, Budapest, Hungary, May10-15, 2009.Revised Selected Papers

Programming Multi-Agent Systems

Jean-Marc Geib

This book contains a refereed collection of revised papers selected from the presentations at the France-Japan Workshop on Object-Based Parallel and Distributed Computation, OBPDC'95, held in Tokyo in June 1995. The 18 full papers included in the book constitute a representative, well-balanced set of timely research contributions to the growing field of object-based concurrent computing. The volume is organized in sections on massively parallel programming languages, distributed programming languages, formalisms, distributed operating systems, dependable distributed computing, and software management.

France-Japan Workshop, OBPDC'95, Tokyo, Japan, June 21 - 23, 1995, Selected Papers

Object-Based Parallel and Distributed Computation

Gaëtan Hadjeres

François-David Pachet

This book is a survey and analysis of how deep learning can be used to generate musical content. The authors offer a comprehensive presentation of the foundations of deep learning techniques for music generation. They also develop a conceptual framework used to classify and analyze various types of architecture, encoding models, generation strategies, and ways to control the generation. The five dimensions of this framework are: objective (the kind of musical content to be generated, e.g., melody, accompaniment); representation (the musical elements to be considered and how to encode them, e.g., chord, silence, piano roll, one-hot encoding); architecture (the structure organizing neurons, their connexions, and the flow of their activations, e.g., feedforward, recurrent, variational autoencoder); challenge (the desired properties and issues, e.g., variability, incrementality, adaptability); and strategy (the way to model and control the process of generation, e.g., single-step feedforward, iterative feedforward, decoder feedforward, sampling). To illustrate the possible design decisions and to allow comparison and correlation analysis they analyze and classify more than 40 systems, and they discuss important open challenges such as interactivity, originality, and structure. The authors have extensive knowledge and experience in all related research, technical, performance, and business aspects. The book is suitable for students, practitioners, and researchers in the artificial intelligence, machine learning, and music creation domains. The reader does not require any prior knowledge about artificial neural networks, deep learning, or computer music. The text is fully supported with a comprehensive table of acronyms, bibliography, glossary, and index, and supplementary material is available from the authors' website.

Jean-Pierre Briot

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