Week 5
Week 5: 2006/9/25 – 2006/9/29
New Challenges for Character-Based AI for Games Isla, Damián, and Bruce Blumberg. “New Challenges for Character-Based AI for Games.” Proc. of the AAAI Spring Symposium on AI and Interactive Entertainment, March 2002. Palo Alto, CA.
In this article Isla and Blumberg suggest the next steps character-based AI needs to take to in order to advance for games. First they discuss perception, what the character can and should be able to sense and the ability to recognize patterns. They suggest separating the actual state of the virtual world and the character's perceptions about the state of the world. Another key is the character's ability to anticipate events based on previously noted patterns. This includes anticipating the player's actions as well as other characters'. Isla and Blumberg point out that the ability of the character to predict incorrectly would add an additional level of enlightenment. They also note, like many others, emotion modeling is important an should play a major role in behavior and decisions the character makes. Finally they discuss learning and memory. They specifically suggest episodic memory, where specific examples of events are stored opposed to cause and effect statistics. Their reasoning for this is it will increase learning speed.
A Layered Brain Architecture for Synthetic Creatures
Isla, Damián, et. al. “ A Layered Brain Architecture for Synthetic Creatures” Proc. of the International Joint Conference on Artificial Intelligence, 2001. Seattle, Washington.
Isla et. al. describes C4, a brain architecture designed after biological systems. The brain architecture implemented as a collection of separate systems that “communicate through an internal blackboard”. It contains a world model that functions as a event distribution system. The world model distributes all events to all creatures in the world and it is up to their individual sensory systems to filter out everything but what they sensory information they can honestly sense. A perception system, which categorizes sensory input, and a working memory, which keeps a history of past sensory input, have also been implemented. With the implementation of a working memory prediction and surprise can also be implemented. Currently surprise has yet to be, but prediction has been and acts as much as a way of anticipating future events, but also as a means to maintain a view of the present state. Isla et. al. have also added an action system that decides and selects the appropriate action as well as a navigation system to direct the creature to where the decided action needs to take place, and a motor system (animation) to get it there.
Isla et. al. have already implemented two projects using C4. The first is sheep|dog, the player acts as the shepherd who interacts with Duncan, a virtual dog, using vocal commands to herd sheep. The other is Clicker, in which the player trains Duncan using the click training technique used to train real dogs. In Isla's article “”New Challenges for Character-Based AI for Games” (2002) he describes many of the same systems that have been successfully implemented in C4. Most notable is the differentiation between the world state and the creature/character's perceived view of the world state (sensory honesty).
Motivation Driven Learning for Interactive Synthetic Characters
Yoon, Song-Yee, Bruce M. Blumberg, and Gerald E. Schneider. “Motivation Driven Learning for Interactive Synthetic Characters.” Proc. of the Fourth International Conference on Autonomous Agents, 2000. Barcelona, Spain.
The authors describe synthetic characters as “3D virtual creatures that are intelligent enough to do and express the right things in a particular situation or scenario.” The have the ability to adapt to their environment by adapting their behaviors and preferences. Yoon, Blumberg, and Schneider have implemented a creature kernel that is causative for the actions of a synthetic character. The creature kernel is comprised of four systems: perception, motivation, behavior, and motor. For the character to function successfully in the virtual world interconnected communication between the four systems is established. Their primary focus is the motivation driven learning system that they've broken down into three methods of learning. Organizational learning updates weights and connections (preference learning) in the networks contained within the creature kernel as well as the overall structure of the network (strategy learning). Concept learning pertains to the beliefs a character has about objects and the world; characters are “born” with some built in concepts. Affective tag formation is the tendency to choose one action over another based on an emotional memory.
Imitation as a First Step to Social Learning in Synthetic Characters: A Graph-based Approach
Buchsbaum, D., and B. Blumberg. “ Imitation as a First Step to Social Learning in Synthetic Characters: A Graph-based Approach.” Proc. of 2005 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 2005. Los Angeles, California.
Buchsbaum and Blumberg discuss their work with a system to allow animated characters to observe an imitate the actions of other characters' movements. They go about explaining what they've accomplished with the implementation of Max and Morris Mouse, two anthropomorphic characters. Max has the ability to imitate and deduce the reasoning behind Morris' actions. Max is pre-equipped a set of poses, and moments that transition between poses creating a posegraph. Max also has synthetic vision that takes input as the graphical rendering of the world from his perspective. The objects he perceives are uniquely color coded, theses objects include Morris' body parts. Max uses Morris' root node as a point of reference for his movements to then be able to apply them to himself by searching through his posegraph to find the best matching poses. Max also has the ability to reason Morris' motivations. Max has a motivationally driven action system that gives him motivations for actions. Similar to how Max determines the action, he searches through the action tuples to find motivations for Morris' actions based on his own experiences.
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