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Motivation
http://images.google.com/q=multi-robot+systems
UT3
Travian
Robotics: Strengths
n Better formal representations of the physical world
n More autonomous planning rather than reliance on scripting
n Belief in the power of integrating machine learning algorithms
Robotics on CFP at the AAAI Conference on AI and Interactive Digital Entertainment (2012 and 2013)
Games: Human-Centered Design
Making experiences fun rather than frustrating Dynamic difficulty
adjustment
Games tell Stories
The power of narrative and character development
Games: Intuitive Control
Dedicated but impatient user base seeks fast control
Games: Modeling Group Behavior
Studying individual communication and trade patterns reveals hidden social structures
Predic'ng Guild Membership in Massively Mul'player Online Games Hamidreza Alvari1, Kiran Lakkaraju2 , Gita Sukthankar1 , and Jon Whetzel2 1 University of Central Florida, Orlando, Florida 2 Sandia Na'onal Labs, Albuquerque, New Mexico
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Rela1ve Contribu1on
Social Ties
Skill Homophily
Exis1ng guild
informa1on
?
Guild predic1on is more accurate when seeded with a few highly central members rather than larger numbers of less influen1al.
Future: Robots, Agents, and People
n Future: scaling up not only the robots but the number of people dealing with the same robots.
n Less focus on point-to-point interaction n Social network and modeling techniques developed for
game analytics may be helpful in understanding the holistic system operation.
n Call to action: ¨ More public datasets ¨ More shared testbeds for running experiments
Intelligent Agents Lab
http://ial.eecs.ucf.edu/ Ph.D. Students n Hamidreza Alvari n Rahmatollah Beheshti n Erfan Davami n Alireza Hajibagheri n Astrid Jackson Alumni Bulent Tastan (Amazon), Ken Laviers (SRSU), JT Folsom-Kovarik (SoarTech), Fahad Shah (Microsoft), Bennie Lewis (Lockheed Martin), Mahsa Maghami (Microsoft), Yuan Chang (Google), Xi Wang (Sonobi) Sponsors: AFOSR YIP, NSF CAREER, DARPA CSSP
