1. Εισαγωγή στη Μουσική Πληροφορική Περίληψη: Μάθημα 1 και 2 Christina Anagnostopoulou chrisa@music.uoa.gr Οι διαφάνειες αυτές είναι στα Αγγλικά

2. Music Informatics Discussion: What is Music Informatics? Which are the main areas?

3. Music Informatics and AI Music Informatics in its biggest part uses Artificial Intelligence techniques to study and reproduce computationally music processes such as comprehension, music analysis, composition, performance, improvisation, and so on. What is Artificial Intelligence?

4. AI is a multi-disciplinary area Philosophy Computer Science Engineering Cognitive Science Linguistics Psychology

5. Questions: What is Artificial Intelligence? (Turing test, chinese room) What is intelligence? What is really AI these days? (New names, GOFAI) How about related research? What is Music and AI?

6. Attempt at a definition AI may be defined as the branch of computer science that is concerned with the automation of intelligent behaviour. Although we can more or less agree on what intelligent behaviour is, what is intelligence? Problems: is it only CS? is it only automation? In reality, a much broader field. Definition might not even be possible! (Question: Can we define the term ‘game’?) Automation or formalisation?

7. On machine intelligence Question: If I simulated intelligent behaviour, have I created an intelligent machine/system/program? If I showed intelligent behaviour, am I intelligent? Behaviourism Turing Test Chinese Room Argument

8. The motivation Create systems that show intelligent behaviour and perform intelligent tasks. For example, solve problems, give diagnoses, speech. Question: What are intelligent tasks? Model and gain understanding on human mental processes by creating cognitive models. For example: creating grammatical sentences, recognising pictures. Philosophical issue: Can we create a truly intelligent (and emotive) machine? Can machines substitute humans in the future?

9. What makes AI really special? Does not have to be numeric! Correct answers not always required. Can tolerate incorrect answers. Control structure separate from domain knowledge. Uses heuristics, and not always explicit steps.

10. Approaches to AI Two main approaches: Symbolic versus sub-symbolic. Symbolic: close to Natural Language, uses words to make calculations. Classical AI. Sub-symbolic: numerical and fuzzy. Some examples: Neural Networks Genetic Programming Agent Systems Two other AI systems’ categories: Automated vs. computer-aided approaches - where applicable.

11. Most important distinction in AI approaches: Rule-based versus Learning approach Rule based: We ‘tell’ a system what to do, by using rules (For example, ‘if… then …’ rules). Learning: By giving a big number of examples, we let the system ‘learn’ on its own. This area of AI is called Machine Learning. Learning can either be supervised, or unsupervised. In supervised learning, we help the computer by telling it what is right and what is wrong. In unsupervised learning we do nothing.

12. Application areas of AI Natural Language Processing (NLP) Speech Robotics Machine Vision Cognitive Modelling (models of the mind and brain) Planning and Control systems (eg air traffic control) Stock market Information retrieval and data mining - various applications Diagnosis – expert systems Automated reasoning AI and Music

13. Example area: Natural Language Processing and its levels of description Phonetics Morphology and lexicon Syntax Semantics based on syntax Discourse analysis Rhetoric Structure Theory Information Retrieval Text mining Automatic Translation

14. The Music parallel How do the levels of description in language relate to music processing? What types of tasks can we study in Music-AI? What could we call musical intelligence? Create a list of tasks for music and AI.

15. Introduction to Knowledge Representation (KR)

16. What is a representation? A representation is any notation or sign that re-presents something to us. Stands for something in the absence of that thing. A substitute. For example: maps, menus, painting, language. Important information in, other OUT. What is a mental representation? A representation of the external world in our mind Many types of mental representations (the main issue in Cognitive Science) Structural approach: units and relations between units Symbolic As in any representation, we find levels of Abstraction

17. Symbolic Representations Are made of: –Units, or symbols –Relations between symbols –Operations for these symbols Example knowledge representation Following conventions of first order predicate logic and Prolog. Lets first show some examples: –John –Happy John –John loves Mary –Mary loves someone –Big chocolate cake

18. Symbolic first order representations Facts and Rules stored in the knowledge base (KB). Facts: –Objects –Properties –Relations Variables and facts. Rules: two parts. First part: premise(s). Second part: conclusion(s). Rules can create new facts.

19. Introduction to tree representations The game: Tic Tac Toe (Triliza) How do we play? How do we represent the whole space? What are the rules?

20. Tasks 1.Discuss whether or not you think it is possible for a computer to understand a natural (human) language. 1.Create a knowledge representation of your family and friends, with facts and rules. Show how they are related, and any other properties you wish. Create a forward deduction of at least 2 levels. 1.Choose a problem/situation from your daily life, and show a good knowledge representation format for it. 1.Previous discussion: Levels of description in NLP: Can you find parallels in music?

21. Properties of KR schemes Abstraction only what information is needed to solve a problem Expressiveness Efficiency (a trade-off?) Natural expression of knowledge helps humans

22. A representation should... handle qualitative knowledge –for example the blocks world allow new knowledge to be inferred from facts and rules allow representation of general principles and specific situations –use of variables, learning, neural nets capture complex semantic meaning and meta-knowledge allow meta-level reasoning –a system should know what it knows, especially in machine learning

23. Representational schemes Many schemes, 4 categories (Mylopoulos and Lavesque 84) Logical representation schemes –uses expressions of formal logic(s) –first order logic most common –implemented in PROLOG Procedural representation schemes –knowledge is represented as set of instructions for solving a problem –if-then rules network representation schemes –graphs with nodes being objects or concepts and arcs being relations structured representation schemes –extend networks by allowing nodes to be complex data types –examples: frames, scripts

24. Grammars and syntactic trees Consider the grammar: sentence  noun phrase + verb phrase noun phrase  noun noun phrase  determiner + noun verb phrase  verb verb phrase  verb + noun phrase Draw the syntactic trees for the sentences: John loves Mary John slept The boy ate the dog

25. References General AI textbooks –F. Luger: Artificial Intelligence: Structures and Strategies for Complex Problem Solving (Papasotiriou). Also older edition, by Luger and Stubblefield is very good. –Stuart Russell and Peter Norvig: Artificial Intelligence: A Modern Approach Also in Greek (115€ - Papasotiriou) –Rob Callan: Artificial Intelligence (50€ - Papasotiriou) –Top ten: http://www.cs.berkeley.edu/~russell/competing.html Music-AI books –E. Miranda: Music and Artificial Intelligence (Non-comprehensive) Papers: search citeseer and google

26. For next week Make a search and find 10 different papers on the broad area of Music and AI. References in academic style. (Look at citeseer). Write down the levels of description in music. Write a paragraph on whether you think it is possible for computers to create Music. What do you think would be the difficulties? Can they be overcome? Using the grammar given above, create the syntactic trees for the following sentences: –Emilios ate the cake. –Anastasia plays the piano. –Maria dances.