This paper reports on progress made in the first 3 years of.ATR's 'CAM-Brain'Project, which aims to use 'evolutionary e.gi...,i.gi' techniques to build/grow/evolve a RAM-and-cellular-automata based...This paper reports on progress made in the first 3 years of.ATR's 'CAM-Brain'Project, which aims to use 'evolutionary e.gi...,i.gi' techniques to build/grow/evolve a RAM-and-cellular-automata based artificial brain consisting of thousands of interconnected neural network modules inside special hardware such as MITs Cellular Automata Machine 'CAM-8,i, or NTT's Content Addressable Memory System 'CAM-System'. The states of a billion (later a trillion) 3D cellular automata cells, and edlions of cellular automata rules which govern their state changes, can be stored relatively cheaply in giga(tera)bytes of RAM. After 3 years work, the CA rules are almost ready. MITt,,'CAM-8' (essentially a serial device) can update 200,000,000 CA cells a second. It is possible that NTT's 'CAM-System' (essentially a massively parallel device) may be able to update a trillion CA cells a second. Hence all the ingredients will soon be ready to create a revolutionary new technology which will allow thousands of evolved neural network modules to be assembled into artificial brains. This in turn will probably create not only a new research field, but hopefully a whole new industry,namely 'brain building'. Building artificial brains with a billion neurons is the aim of ATR's 8 year i,CAM-B,ai.,' research project, ending in 2001.展开更多
This paper presents pattern formation in generalized cellular automata (GCA) by varying parameters of classic “game of life”. Different dynamic behaviors are classified. The influence of remembrance of dynamic behav...This paper presents pattern formation in generalized cellular automata (GCA) by varying parameters of classic “game of life”. Different dynamic behaviors are classified. The influence of remembrance of dynamic behavior of GCA is also studied. Experiments show the emergence of the self-organizing patterns that is analogous with life forms at the edge of chaos, which consist of certain nontrivial structure and go through periods of growth, maturity and death. We describe these experiments and discuss their potential as alternative way for creating artificial life and generative art, and as a new method for pattern genesis.展开更多
Research on evacuation simulation and modeling is an important and urgent issue for emergency management.This paper presents an evacuation model based on cellular automata and social force to simulate the evacuation d...Research on evacuation simulation and modeling is an important and urgent issue for emergency management.This paper presents an evacuation model based on cellular automata and social force to simulate the evacuation dynamics.Attractive force of target position,repulsive forces of individuals and obstacles,as well as congestion are considered in order to simulate the interaction among evacuees and the changing environment.A visual-guidance-based artificial bee colony algorithm is proposed to optimize the evacuation process.Each evacuee moves toward exits with the guidance of leading bee in his/her visual field.And leading bee is selected according to comprehensive factors including distance from the current individual,the number of obstacles and congestion,which avoids the randomness of roulette mechanism used by basic artificial bee colony algorithm.The experimental results indicate that the proposed model and algorithm can achieve effective performances for indoor evacuation problems with a large number of evacuees and obstacles,which accords with the actual evacuation situation.展开更多
The model of EQUnn (equivalent neural network of the CAM-Brain model) is proposed. With the help of EQUnn model, it is proved that the CAM-Brain can solve the XOR problem.
文摘This paper reports on progress made in the first 3 years of.ATR's 'CAM-Brain'Project, which aims to use 'evolutionary e.gi...,i.gi' techniques to build/grow/evolve a RAM-and-cellular-automata based artificial brain consisting of thousands of interconnected neural network modules inside special hardware such as MITs Cellular Automata Machine 'CAM-8,i, or NTT's Content Addressable Memory System 'CAM-System'. The states of a billion (later a trillion) 3D cellular automata cells, and edlions of cellular automata rules which govern their state changes, can be stored relatively cheaply in giga(tera)bytes of RAM. After 3 years work, the CA rules are almost ready. MITt,,'CAM-8' (essentially a serial device) can update 200,000,000 CA cells a second. It is possible that NTT's 'CAM-System' (essentially a massively parallel device) may be able to update a trillion CA cells a second. Hence all the ingredients will soon be ready to create a revolutionary new technology which will allow thousands of evolved neural network modules to be assembled into artificial brains. This in turn will probably create not only a new research field, but hopefully a whole new industry,namely 'brain building'. Building artificial brains with a billion neurons is the aim of ATR's 8 year i,CAM-B,ai.,' research project, ending in 2001.
基金Project supported by the National Basic Research Program (973) of China (No. 2002CB312103), and Excellent Young Teacher Program of MOE, China
文摘This paper presents pattern formation in generalized cellular automata (GCA) by varying parameters of classic “game of life”. Different dynamic behaviors are classified. The influence of remembrance of dynamic behavior of GCA is also studied. Experiments show the emergence of the self-organizing patterns that is analogous with life forms at the edge of chaos, which consist of certain nontrivial structure and go through periods of growth, maturity and death. We describe these experiments and discuss their potential as alternative way for creating artificial life and generative art, and as a new method for pattern genesis.
基金This work was supported by the National Natural Science Foundation of China(No.61772180)the Key R&D Plan of Hubei Province(No.2020BHB004 and No.2020BAB012)the Natural Science Foundation of Hubei Province(No.2020CFB798)。
文摘Research on evacuation simulation and modeling is an important and urgent issue for emergency management.This paper presents an evacuation model based on cellular automata and social force to simulate the evacuation dynamics.Attractive force of target position,repulsive forces of individuals and obstacles,as well as congestion are considered in order to simulate the interaction among evacuees and the changing environment.A visual-guidance-based artificial bee colony algorithm is proposed to optimize the evacuation process.Each evacuee moves toward exits with the guidance of leading bee in his/her visual field.And leading bee is selected according to comprehensive factors including distance from the current individual,the number of obstacles and congestion,which avoids the randomness of roulette mechanism used by basic artificial bee colony algorithm.The experimental results indicate that the proposed model and algorithm can achieve effective performances for indoor evacuation problems with a large number of evacuees and obstacles,which accords with the actual evacuation situation.
文摘The model of EQUnn (equivalent neural network of the CAM-Brain model) is proposed. With the help of EQUnn model, it is proved that the CAM-Brain can solve the XOR problem.
