BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Sabre//Sabre VObject 4.6.0//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Europe/Zurich X-LIC-LOCATION:Europe/Zurich TZURL:http://tzurl.org/zoneinfo/Europe/Zurich BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:19810329T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:19961027T030000 RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:news199@dmi.unibas.ch DTSTAMP;TZID=Europe/Zurich:20181113T175346 DTSTART;TZID=Europe/Zurich:20180227T121500 SUMMARY:Computer Science Colloquium: Nathan Sturtevant DESCRIPTION:Abstract: What does it take to build a high-quality pathfinding engine? One might think that pathfinding in games is a simple case of usi ng A*\, which finds shortest paths. But\, in practice\, there are many oth er considerations for finding high-quality paths quickly in dynamic enviro nments. This talk will give an overview of the techniques required to buil d the pathfinding engine of Dragon Age: Origins. Five iterations of improv ements will be presented\, starting from basic pathfinding approaches\, an d moving to a final system that creates high-quality\, smooth paths. To co nclude\, we will discuss open challenges in pathfinding and what it would take to create a pathfinding system that finds human-quality paths.\\r\\nS peaker Bio: Nathan Sturtevant is an Associate Professor in the Computer Sc ience Department at the University of Denver. His scientific research focu ses on search in Artificial Intelligence. This includes work on heuristic and combinatorial search for single and multiple agents\, including bidire ctional search\, automated abstraction\, heuristics\, refinement search\, search for game design\, heuristic learning\, inconsistent heuristics\, co operative search\, large-scale and parallel search.Particular applications include pathfinding and planning in memory-constrained real-time environm ents (e.g. commercial video games) as well as algorithms for building and using memory-based heuristics via large-scale search. Other work considers theoretical and practical issues in games with more than two players\, in cluding opponent modeling\, learning\, and imperfect information. X-ALT-DESC:\nAbstract: What does it take to build a high-quality pat hfinding engine? One might think that pathfinding in games is a simple cas e of using A*\, which finds shortest paths. But\, in practice\, there are many other considerations for finding high-quality paths quickly in dynami c environments. This talk will give an overview of the techniques required to build the pathfinding engine of Dragon Age: Origins. Five iterations o f improvements will be presented\, starting from basic pathfinding approac hes\, and moving to a final system that creates high-quality\, smooth path s. To conclude\, we will discuss open challenges in pathfinding and what i t would take to create a pathfinding system that finds human-quality paths .\nSpeaker Bio: Nathan Sturtevant is an Associate Professor in the Computer Science Department at the University of Denver. His scientific re search focuses on search in Artificial Intelligence. This includes work on heuristic and combinatorial search for single and multiple agents\, inclu ding bidirectional search\, automated abstraction\, heuristics\, refinemen t search\, search for game design\, heuristic learning\, inconsistent heur istics\, cooperative search\, large-scale and parallel search.
Partic ular applications include pathfinding and planning in memory-constrained r eal-time environments (e.g. commercial video games) as well as algorithms for building and using memory-based heuristics via large-scale search. Oth er work considers theoretical and practical issues in games with more than two players\, including opponent modeling\, learning\, and imperfect info rmation. DTEND;TZID=Europe/Zurich:20180227T140000 END:VEVENT END:VCALENDAR