Web Version of the PPSN VII Invited Talk (Held on September 9, 2002 by Alexander Nareyek) Because these are only the slides without any verbal parts, it might be hard to study the document if you did not attend the talk. Some basic comments were added in the form: # Comment # Please note that this version of the talk does not contain any videos/music to keep the size at an appropriate level for download. The pictures included in this document were mostly taken from the world wide web. If you identify any copyright issues, please let me know

Title Is Research Going in the Right Direction? Abstract What about the general direction of our research and the methodology used? Is it in line with real-world application requirements? Or are we focusing on issues of secondary importance and missing crucial requirements? Questions like these will be raised in the talk, and the answers will not always be satisfactory. Throughout the talk, the booming application domain of computer games will serve as an example because its extreme requirements regarding algorithms and production enable attention to be drawn to many deficiencies in the research sphere. Bio Alexander Nareyek studied computer science at the TU Berlin, where he also obtained his Ph.D. He won the GMD's 1997 Best Degree Thesis Award, was a winner of the BMWi's Founders Competition Multimedia 1998 and was awarded an enhanced Ph.D. scholarship by the German Research Foundation (DFG). He is involved in many AI events, e.g. as organizer of several workshops on planning topics and as chair of the IGDA's AI Interface Standards Committee. Since 2002, he has been guest researcher at CMU on an Emmy Noether fellowship.

Is Research Going in the Right Direction? Alexander Nareyek Carnegie Mellon University Or: Wheres the Cheese?

…the Drosophila of Artificial Intelligence… # The era of games like chess as a suitable research tool # # is over; they lacks very important features to be considered # # in todays research. #

# Games like Pac Man are already 20 years old. Technology # # progress in the domain of computer games is incredible, and # # they provide highly complex environments. #

Action Games Role-Play Games Strategy Games Sports Games # Overview of game genres & technology – the videos are not # # included. You may visit web pages like # # to download movie/demo samples. #

Editing Your Test Scenarios # Demo not included; many major computer games provide you with # # very advanced editors and scripting languages to implement your # # own AI behavior. #

Creatures – Genetics at Work # Demo not included; an example of a game applying # # evolutionary techniques, however, not as a search technique. #

# Nearly no commercial game applies any of our search # # techniques! What are the reasons? #

PPSN VI Real Time Quality Time ? t 13% 30% 0% 57% # Hardly any research on engineering anytime behavior. # # Given response time distribution # # Automatic anytime # # curve optimization #

Dynamics If anything can go wrong, it will. Edward A. Murphy, Jr. PPSN VI 12% # Things go wrong and our world is constantly changing. # # Algorithms/representations must be able to incorporate this! #

Conceptual Flexibility # Not only are real-world applications always a bit different # # and require algorithmic/representational adaptations. # # Design documents also rarely stay the same during development. # # Lets represent the design document as a painting… #

Conceptual Flexibility # Some weeks later… #

Conceptual Flexibility PPSN VI 17% # And two weeks before the final milestone. #

The One at a Time Folks Start with one feature and add the others stepwise. # Many people want to optimize one feature before whey worry about others. #

Efficiency vs. Conceptual Flexibility public static final int fac(int n) { if (n != 0) { return n * fac(n - 1); } else { return 1; } An Example: Calculating the Factorial Function # Textbook version. #

Efficiency vs. Conceptual Flexibility public static final int fac(int n) { int f = 1; do { f = f * n; } while (--n > 0); return f; } Requires only 60.39% of first solutions time! # Lets get rid of the expensive recursion. #

Efficiency vs. Conceptual Flexibility public static final int fac(int n) { switch (n) { case 1: return 1; case 2: return 2; case 3: return 6; case 4: return 24; case 5: return 120; case 6: return 720; case 7: return 5040; case 8: return 40320; case 9: return ; case 10: return ; case 11: return ; case 12: return ; } return 0; } Requires only 32.02% of first solutions time! # We can only compute values up # # to 12 because of an int overflow # # otherwise! #

Efficiency vs. Conceptual Flexibility public static final int fac(int n) { if (n < 5) { if (n < 3) { if (n == 1) { return 1; } else { return 2; } } else { if (n == 3) { return 6; } else { return 24; } } else { … Requires only 24.51% of first solutions time! # Optimizing value retrieval # # by a balanced tree. #

Efficiency vs. Conceptual Flexibility static final int[] facArray = new int[13]; static { facArray[1] = 1; facArray[2] = 2; facArray[3] = 6; facArray[4] = 24; facArray[5] = 120; facArray[6] = 720; facArray[7] = 5040; facArray[8] = 40320; facArray[9] = ; facArray[10] = ; facArray[11] = ; facArray[12] = ; } public static final int fac(int n) { return facArray[n]; } Requires only 2.18% of first solutions time! # Further optimizing value # # retrieval by an external array. # # Great benchmark to be # # published, isnt it? # # But not exactly a flexible # # solution anymore… #

Multi-Objective Algorithm Design Feature A Feature B # Algorithm design is a multi-objective task and focusing on only one # # feature such as efficiency will take you away from possible applications! #

DragonBreath Engine Demo # Demo not included; engine is free and can be downloaded via: # # # # Highly flexible by being based on the paradigm of constraint programming # # and easily extensible/adaptable by a very modular concept. Complex dynamic # # changes can be incorporated during search. Anytime behavior can be engineered # # by switching between given heuristics, adding new ones, or applying learning # # techniques to adapt to the required behavior. #

Global Search Control Constraint Improvement Heuristic Improvement Heuristic Improvement Heuristic Choice of Heuristic (Learning) Variable report value changes assign new value request improvement report cost changes Concept of the DragonBreath Engine

Conclusion # Go for a mix of different cheeses! Efficiency is just one of the important features! # Consider to explore new dimensions in the algorithm design space! Applications like computer games are helpful and fun! Open Positions ! ( students, docs & post-docs )