SIGGRAPH 2000 Course #24
Interactions with animated characters should be through the modalities that we share among real people such as language, gesture, and shared perceptions of the world. This course will explore several ways that real-time, animated, embodied characters can be given more human-like intelligence and communication skills so that they can act, react, make decisions, and take initiatives.
As real-time characters become almost commonplace, we begin to face the next challenge of making those characters interact with real people. Interactions with these characters should be through the modalities that we share among real people: especially, language, gesture, and shared perceptions of the world. This course will explore several ways that real-time, animated, embodied characters can be given more intelligence and communication skills so that they can act, react, make decisions, and take initiatives. Applications to collaborative groups, interactive training, and smarter games will be addressed.
Actions required for animated agents (faces, arms, legs, and eyes.) Knowledge and action representation. Commonsense and logical reasoning. Agent architectures. Learning. Smart conversations. Agents for pedagogical interaction. Managing multi-agent interactions. Language and gesture as control modalities.
Some experience with graphical modeling and animating human-like characters would be an asset, but not strictly essential.
8:30 - 8:35 Badler (5 min.) Welcome and Overview 8:35 - 10:00 Badler (85 min.) Action Primitives - Attribute Taxonomy for smart embodied agents - Application Domains Action Representation - Parallel Transition Networks - Parameterized Action Representation (PAR) Agent Models - Components - Construction Natural Language Interfaces - Action dictionaries - Standing Orders Cognitive and Empirical Models of Behavior - Visual Attention - Agent manner via the EMOTE model - Building PARs by demonstration 10:00 - 10:15 (break) (15 min.) 10:15 - 11:45 Cassell (90 min.) Conversational Agents What It Means to be "Smart" about Conversation - conversation is composed of propositional & interactional smarts - conversation is advanced by verbal and nonverbal means How Humans are Smart about Conversation - we pick up on very tiny cues in both speech and non-verbal channel The Role of Conversational Smarts in Animated Agents - Increased smoothness of interaction with humans - Less disfluency - Allows both system & human to take the initiative in the interaction Agent Integration - How to incorporate conversational smarts into agent architectures - KQML frames for verbal & nonverbal, propositional & interactional data - Maintaining verbal and non-verbal focus throughout the architecture - Modeling social and goal-oriented behaviors - Some examples of smart conversational agents 11:45 - 12:00 Questions and Issues 12:00 - 1:30 (lunch) (90 min.) 1:30 - 2:30 Funge (60 min.) Introduction to Cognitive Modeling Case Study 1: Prehistoric World - Knowledge Representation - Planning - Goal-directed Behavior Specification Case Study 2: Cinematography Case Study 3: Undersea World - System Architecture - Uncertainty - IVE Fluents Conclusion 2:30 - 3:00 Rickel (30 min.) Task-Oriented Collaboration - Plan construction, revision and execution - Plan recognition - Task-oriented dialogue - Teams 3:00 - 3:15 (break) (15 min.) 3:15 - 3:45 Rickel (continued) (30 min.) 3:45 - 4:45 Blumberg (60 min.) Learning the Consequences of Behavior and Learning as Behavior Why should characters learn? What sorts of things should they learn? How can they learn the things they should? Using animal learning and training as a model Types of learning: Context, Consequences, Control What animals learn: consequences of action Terms of the trade: Reinforcement, Behavior, Context If it is so hard how do you train an animal to do anything? The secrets of great animal trainers - Event markers - Shaping - Behavior, then context - Generalization - Training for variability vs. consistency Examples of computational learning inspired by learning in animals Lessons and Caveats 4:45 - 5:00 Questions and Issues (15 min.) =============================================================================
NORMAN I. BADLER Director, Center for Human Modeling and Simulation Professor, Computer and Information Science Department 200 South 33rd St. University of Pennsylvania Philadelphia, PA 19104-6389 Tel: 1-215-898-5862 Fax: 1-215-573-7453 firstname.lastname@example.org http://www.cis.upenn.edu/~badler Dr. Norman I. Badler is Professor of Computer and Information Science and Director of the Center for Human Modeling and Simulation at the University of Pennsylvania. Active in computer graphics since 1968, his research focuses on human figure modeling, manipulation, and animation. He is the originator of the ``Jack'' software system (now a commercial product from Engineering Animation, Inc.). Badler received the BA degree in Creative Studies Mathematics from the University of California at Santa Barbara in 1970, the MSc in Mathematics in 1971, and the Ph.D. in Computer Science in 1975, both from the University of Toronto. JOHN FUNGE Research Scientist Sony Computer Entertainment America 919 East Hillsdale Boulevard Foster City, California 94404-2175 Tel: 1-650-655-5658 Fax: 1-650-655-8180 email@example.com http://www.cs.toronto.edu/~funge/ John Funge recently joined Sony Computer Entertainment America (SCEA) where he works in a group that performs advanced research into technology for future computer games. Previously John was a member of Intel's Microcomputer Research Lab. He received a B.Sc. in Mathematics from King's College London in 1990, an M.Sc. in Computer Science from Oxford University in 1991, and a Ph.D. in Computer Science from the University of Toronto in 1997. For his Ph.D. John successfully developed a new approach to high-level control of characters in games and animation. John is the author of numerous technical papers and his new book "AI for Games and Animation: A Cognitive Modeling Approach" is one of the first to take an academic look at AI techniques in the context of computer games and animation. His current research interests include computer animation, computer games, smart networked devices, interval arithmetic and knowledge representation. BRUCE BLUMBERG Assistant Professor Synthetic Characters Group The Media Lab Massachusetts Institute of Technology E15-311, 20 Ames St. Cambridge MA 02139 Tel: 1-617-253-9832 Fax: 1-617-253-6205 firstname.lastname@example.org http://www.media.mit.edu/~bruce Bruce Blumberg, is an assistant professor and head of the Synthetic Characters Group at the Media Lab of MIT. Bruce is a well known researcher in the area of autonomous animated characters focusing on the development of computational models of behavior, motivation, perception, emotion and adaptation inspired by work in animal behavior, psychology and artificial intelligence. His group is a frequent contributor to the interactive venues of Siggraph, including (void *): A Cast of Characters at Siggraph '99, SWAMPED at Siggraph '98, and ALIVE at Siggraph 95 and 93. He has a Master's from the Sloan School at MIT and a B.A. from Amherst College. Prior to coming to the lab he held positions at Apple Computer Inc, and NeXT Inc. JUSTINE CASSELL MIT Media Lab, E15-315 20 Ames Street Cambridge, MA 02139 Tel: 1-617-253-4899 Fax: 1-617-253-6215 email@example.com http://www.media.mit.edu/~justine/ Justine Cassell is faculty at MIT's Media Laboratory. After ten years studying human communication through microanalysis of videotaped data, Cassell began to bring her knowledge of human conversation to the design of computational systems, co-designing the first autonomous animated agent with speech, gesture, intonation and facial expression in 1994. She is currently implementing the third generation of embodied conversational character. The architecture for this new agent is based on conversational functions, allowing the system to exploit users' natural speech, gesture and head movement in the input to organize conversation, and to respond with autonomous appropriate verbal and nonverbal behaviors of its own. JEFF RICKEL USC Information Sciences Institute 4676 Admiralty Way, Suite 1001 Marina del Rey, CA 90292-6695 Tel: 1-310-448-9124 Fax: 1-310-822-0751 firstname.lastname@example.org http://www.isi.edu/isd/rickel Jeff Rickel is a Project Leader at the Information Sciences Institute and a Research Assistant Professor in the Department of Computer Science at the University of Southern California. He has been active in artificial intelligence research since 1985, when he joined Texas Instruments (TI) to study the use of artificial intelligence in industrial automation. During his years at TI, he published on topics ranging from knowledge-based planning and simulation to automated production scheduling and intelligent tutoring. Dr. Rickel received his Ph.D. in Computer Science from the University of Texas in 1995 for his work on automated modeling of physical systems. Since then, his research has focused on animated, intelligent agents for training in virtual reality. This work has resulted in STEVE, a virtual human that has been featured in academic publications as well as on CNN, the Discovery Channel, BBC, and magazines and newspapers around the world.