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Craig W. Reynolds

Bio: Craig W. Reynolds is an academic researcher from Sony Computer Entertainment. The author has contributed to research in topics: Population & Computer facial animation. The author has an hindex of 11, co-authored 21 publications receiving 10272 citations. Previous affiliations of Craig W. Reynolds include University of California, Santa Cruz & Electronic Arts.

Papers
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Proceedings ArticleDOI
01 Aug 1987
TL;DR: In this article, an approach based on simulation as an alternative to scripting the paths of each bird individually is explored, with the simulated birds being the particles and the aggregate motion of the simulated flock is created by a distributed behavioral model much like that at work in a natural flock; the birds choose their own course.
Abstract: The aggregate motion of a flock of birds, a herd of land animals, or a school of fish is a beautiful and familiar part of the natural world. But this type of complex motion is rarely seen in computer animation. This paper explores an approach based on simulation as an alternative to scripting the paths of each bird individually. The simulated flock is an elaboration of a particle systems, with the simulated birds being the particles. The aggregate motion of the simulated flock is created by a distributed behavioral model much like that at work in a natural flock; the birds choose their own course. Each simulated bird is implemented as an independent actor that navigates according to its local perception of the dynamic environment, the laws of simulated physics that rule its motion, and a set of behaviors programmed into it by the "animator." The aggregate motion of the simulated flock is the result of the dense interaction of the relatively simple behaviors of the individual simulated birds.

7,365 citations

OtherDOI
01 Jul 1998
TL;DR: This paper explores an approach based on simulation as an alternative to scripting the paths of each bird individually, an elaboration of a particle system, with the simulated birds being the particles.
Abstract: The aggregate motion of a flock of birds, a herd of land animals, or a school of fish is a beautiful and familiar part of the natural world. But this type of complex motion is rarely seen in computer animation. This paper explores an approach based on simulation as an alternative to scripting the paths of each bird individually. The simulated flock is an elaboration of a particle system, with the simulated birds being the particles. The aggregate motion of the simulated flock is created by a distributed behavioral model much like that at work in a natural flock; the birds choose their own course. Each simulated bird is implemented as an independent actor that navigates according to its local perception of the dynamic environment, the laws of simulated physics that rule its motion, and a set of behaviors programmed into it by the "animator." The aggregate motion of the simulated flock is the result of the dense interaction of the relatively simple behaviors of the individual simulated birds.

2,350 citations

Proceedings ArticleDOI
01 Jul 1982
TL;DR: Ideas from programming styles used in current Artificial Intelligence research inspired the design of ASAS, which is in fact an extension to the Lisp programming environment.
Abstract: A technique and philosophy for controlling computer animation is discussed. Using the Actor/Scriptor Animation System (ASAS) a sequence is described by the animator as a formal written SCRIPT, which is in fact a program in an animation/graphic language. Getting the desired animation is then equivalent to “debugging” the script. Typical images manipulated with ASAS are synthetic, 3D perspective, color, shaded images. However, the animation control techniques are independent of the underlying software and hardware of the display system, so apply to other types (still, B&W, 2D, line drawing ...). Dynamic (and static) graphics are based on a set of geometric object data types and a set of geometric operators on these types. Both sets are extensible. The operators are applied to the objects under the control of modular animated program structures. These structures (called actors) allow parallelism, independence, and optionally, synchronization, so that they can render the full range of the time sequencing of events. Actors are the embodiment of imaginary players in a simulated movie. A type of animated number can be used to drive geometric expressions (nested geometrical operators) with dynamic parameters to produce animated objects. Ideas from programming styles used in current Artificial Intelligence research inspired the design of ASAS, which is in fact an extension to the Lisp programming environment. ASAS was developed in an academic research environment and made the transition to the “real world” of commercial motion graphics production.

239 citations

Proceedings Article
09 Aug 1993
TL;DR: Coordinated motion in a group of simulated critters can evolve under selection pressure from an appropriate fitness criteria under the Genetic Programming paradigm.
Abstract: Coordinated motion in a group of simulated critters can evolve under selection pressure from an appropriate fitness criteria. Evolution is modeled with the Genetic Programming paradigm. The simulated environment consists of a group of critters, some static obstacles, and a predator. In order to survive, the critters must avoid collisions (with obstacles as well as with each other) and must avoid predation. They must steer a safe path through the dynamic environment using only information received through their visual sensors. The arrangement of visual sensors, as well as the mapping from sensor data to motor action is determined by the evolved controller program. The motor model assumes an innate constant forward velocity and limited steering. The predator preferentially targets isolated “stragglers” and so encourages aggregation. Fitness is based on the sum of all critter lifetimes.

167 citations

Proceedings ArticleDOI
30 Jul 2006
TL;DR: An implementation of a scalable multi-processor approach to large, fast crowd simulations, as in Quinn et al. 2003, is described for PLAYSTATION®3 which supports simulation and display of simple crowds of up to 15,000 individuals at 60 frames per second.
Abstract: Crowds and other flock-like group motion are often modeled as interacting particle systems. These multi-agent simulations are computationally expensive because each agent must consider all of the others, if only to identify its neighbors. For large crowds, simple implementations are too slow since computation grows as the square of agent population. Faster approaches often rely on spatial hashing where a partitioning of space is used to accelerate crowd simulation. This same partitioning can form the basis of a scalable multi-processor approach to large, fast crowd simulations, as in [Quinn et al. 2003]. This paper describes an implementation of that approach for PLAYSTATION®3 which supports simulation and display of simple crowds of up to 15,000 individuals at 60 frames per second.

153 citations


Cited by
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Proceedings ArticleDOI
06 Aug 2002
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Abstract: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.

35,104 citations

01 May 1993
TL;DR: Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems.
Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

29,323 citations

Journal ArticleDOI
TL;DR: A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.
Abstract: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed The relationships between particle swarm optimization and both artificial life and genetic algorithms are described

18,439 citations

Proceedings ArticleDOI
04 Oct 1995
TL;DR: The optimization of nonlinear functions using particle swarm methodology is described and implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm.
Abstract: The optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm. Benchmark testing of both paradigms is described, and applications, including neural network training and robot task learning, are proposed. Relationships between particle swarm optimization and both artificial life and evolutionary computation are reviewed.

14,477 citations

Journal ArticleDOI
TL;DR: A distinctive feature of this work is to address consensus problems for networks with directed information flow by establishing a direct connection between the algebraic connectivity of the network and the performance of a linear consensus protocol.
Abstract: In this paper, we discuss consensus problems for networks of dynamic agents with fixed and switching topologies. We analyze three cases: 1) directed networks with fixed topology; 2) directed networks with switching topology; and 3) undirected networks with communication time-delays and fixed topology. We introduce two consensus protocols for networks with and without time-delays and provide a convergence analysis in all three cases. We establish a direct connection between the algebraic connectivity (or Fiedler eigenvalue) of the network and the performance (or negotiation speed) of a linear consensus protocol. This required the generalization of the notion of algebraic connectivity of undirected graphs to digraphs. It turns out that balanced digraphs play a key role in addressing average-consensus problems. We introduce disagreement functions for convergence analysis of consensus protocols. A disagreement function is a Lyapunov function for the disagreement network dynamics. We proposed a simple disagreement function that is a common Lyapunov function for the disagreement dynamics of a directed network with switching topology. A distinctive feature of this work is to address consensus problems for networks with directed information flow. We provide analytical tools that rely on algebraic graph theory, matrix theory, and control theory. Simulations are provided that demonstrate the effectiveness of our theoretical results.

11,658 citations