Complex Group Behaviors Research Articles

Huddling by rat pups: Ontogeny of individual and group behavior

A full account of behavioral development in rats must include the ontogeny of both individual and group behavior. Most of our accumulated knowledge, however, pertains to individual ontogenesis. Group behavior and its development are readily seen in the huddling behavior of rat pups. A rat huddle is an entity with characteristics and capabilities distinct from those of the individuals that comprise it. The huddle is a natural context for acquiring olfactory preferences for species odors. Olfactory learning in a huddle involves thermal and tactile stimulation from the mother's body but, surprisingly, not the rewards of suckling or of milk transfer. Although there is complete developmental continuity of huddling behavior, the sensory controls of huddling change dramatically during the first 2 weeks of postnatal life. Huddling behavior is initially controlled by thermal cues ("physiological huddling") and then becomes dominated by olfactory stimuli ("filial huddling"). The complex group behavior of huddling was modeled successfully with computational methods. Group behavior emerges from individual interactions, guided entirely by rules of individual behavior (no rules for group behavior). Three simple rules of autonomous activity/inactivity can spawn the patterns of aggregon formation displayed by groups of 7-day-old pups, but not by 10-day-olds. The developmental change evident by Day 10 requires adding a rule by which each individual is affected by the activity state of adjacent pups. Group behavior responded to manipulations of central oxytocin on Day 10, but not on Day 7.

Compound behaviors in pheromone robotics

We are pursuing techniques for coordinating the actions of large numbers of small-scale robots to achieve useful large-scale results in surveillance, reconnaissance, hazard detection, and path finding. Using the biologically inspired notion of “virtual pheromone” messaging, we describe how many coordinated activities can be accomplished without centralized control. By virtue of this simple messaging scheme, a robot swarm can become a distributed computing mesh embedded within the environment, while simultaneously acting as a physical embodiment of the user interface. We further describe a set of logical primitives for controlling the flow of virtual pheromone messages throughout the robot swarm. These enable the design of complex group behaviors mediated by messages exchanged between neighboring robots.

The Design and Role of Synthetic Creative Traits in Artificial Ant Colonies

Previous and current research on ant colonies have focused on the emergence of complex and sophisticated group-behaviors that are characteristic of the whole colony, starting from simple rules characterizing each individual ant. Examples of emergent behaviors include foraging and nest building. While social scientists, biologists, and physicists have focused on observing and analyzing the behavior of actual ants, researchers in computer science have investigated through modeling and simulation of synthetic ants on the computer, the emergent behavior in a collection of simple robots, simulated evolution of computer programs, and the formation of pheromone patterns during foraging under unlimited food supply. This paper has two objectives. First, it presents a systematic study, through behavior modeling and simulation, of the influence of the presence or absence of pheromone, the duration interval of pheromone, the extent of the food supply at the food sources, the size of the colony, and the search strategy employed during foraging, for a given geometry and a finite number of food sources, on the performance of the ant colony. The second and most important objective of this paper is to scientifically study the nature of creativity by modeling synthetic creativity in an ant colony, simulating it on a computer system, and measuring its impact on performance through innovative metric design. The study is motivated by leading thinkers, throughout time, who have strongly emphasized the importance of creativity over intelligence. While an exact and universal definition of creativity is elusive, the important characteristics include originality, intuition, and imagination. Creativity is best understood through its manifestations as novel and radically different ideas that transcend current knowledge and reasoning. In this study, two synthetic creativity traits are introduced into select individual ants of a colony by imparting to them a foraging behavior that is radically different from the normal behavior. Under the first trait, the creative ants choose to ignore the existing pheromone trails and search for food sources. Under the second trait, a creative ant shares its knowledge of food source location, after discovery, with all other creative ants, so that together they can focus solely on discovering new food sources. Analysis of the simulation results reveal that a creative trait coupled with the underlying parameters of the ant colony may cause the foraging completion time metric, i.e. the time to collect food from all sources, to be marginally better or weaker than the normal colony. Contrary to intuition, the completion time metric worsens when, in a fixed sized colony, the number of creative ants, relative to normal ants, becomes excessive. Furthermore, for a given finite geometry, finite food supply, and a fixed ratio of the number of creative to normal ants, the impact of creativity on the foraging performance is dependent on the underlying parameters, and may either continue to improve, remain unchanged, or decrease, as the size of the colony increases. Finally, while the creative ants expend, on an average, two to three times more energy than the normal ants, the ant colony with the creative ants discovers more food sources, in less time and, under certain circumstances, a creative colony discovers all food sources which a normal colony can never achieve.

Evolving complex group behaviors using genetic programming with fitness switching

Genetic programming provides a useful tool for emergent computation and artificial life research. However, conventional genetic programming is not efficient enough to solve realistic multiagent tasks consisting of several emergent behaviors that need to be coordinated in the proper sequence. In this paper, we describe a novel method, called fitness switching, for evolving composite cooperative behaviors in multiple robotic agents using genetic programming. The method maintains a pool of basis fitness functions which are switched from simpler ones to more complex ones. The performance is demonstrated and evaluated in the context of a table transport problem. Experimental results show that the fitness switching method is an effective mechanism for evolving collective behaviors which can not be solved by simple genetic programming.

Polycyclic motion history of some Gulf Coast growth faults from high-resolution displacement analysis

Research Article| September 01, 1998 Polycyclic motion history of some Gulf Coast growth faults from high-resolution displacement analysis Joe Cartwright; Joe Cartwright 1Centre for Petroleum Studies, T. H. Huxley School of Environment, Earth Sciences, and Engineering, Imperial College of Science, Technology, and Medicine, Royal School of Mines, Prince Consort Road, London SW7 2BP, United Kingdom Search for other works by this author on: GSW Google Scholar Renaud Bouroullec; Renaud Bouroullec 1Centre for Petroleum Studies, T. H. Huxley School of Environment, Earth Sciences, and Engineering, Imperial College of Science, Technology, and Medicine, Royal School of Mines, Prince Consort Road, London SW7 2BP, United Kingdom Search for other works by this author on: GSW Google Scholar David James; David James 1Centre for Petroleum Studies, T. H. Huxley School of Environment, Earth Sciences, and Engineering, Imperial College of Science, Technology, and Medicine, Royal School of Mines, Prince Consort Road, London SW7 2BP, United Kingdom Search for other works by this author on: GSW Google Scholar Howard Johnson Howard Johnson 1Centre for Petroleum Studies, T. H. Huxley School of Environment, Earth Sciences, and Engineering, Imperial College of Science, Technology, and Medicine, Royal School of Mines, Prince Consort Road, London SW7 2BP, United Kingdom Search for other works by this author on: GSW Google Scholar Geology (1998) 26 (9): 819–822. https://doi.org/10.1130/0091-7613(1998)026<0819:PMHOSG>2.3.CO;2 Article history first online: 02 Jun 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Joe Cartwright, Renaud Bouroullec, David James, Howard Johnson; Polycyclic motion history of some Gulf Coast growth faults from high-resolution displacement analysis. Geology 1998;; 26 (9): 819–822. doi: https://doi.org/10.1130/0091-7613(1998)026<0819:PMHOSG>2.3.CO;2 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyGeology Search Advanced Search Abstract This paper describes some simple graphical techniques for analyzing the kinematic evolution of growth faults and presents a case study of the late Pleistocene–recent history of a group of 17 faults from offshore Texas. Throw versus depth plots derived from depth-converted high-resolution seismic data were used to define growth histories. The faults exhibit polycyclic kinematic behavior, with at least three cycles of active fault growth separated by periods of inactivity. This cyclic behavior correlates broadly with three transgressive-regressive cycles along this part of the Gulf Coast. In detail, however, we find that activity of closely spaced faults in the array can either be in phase or out of phase with neighboring faults. This complex group behavior is attributed to the geometry of the detachment surface, pore-fluid pressure distribution, loading, and friction. This study demonstrates that although a general correlation with sediment loading may exist, individual fault activity is unlikely to correlate with periods of maximum sediment accumulation. This content is PDF only. Please click on the PDF icon to access. First Page Preview Close Modal You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

Artificial Fishes: Autonomous Locomotion, Perception, Behavior, and Learning in a Simulated Physical World

This article develops artificial life patterned after animals as evolved as those in the superclass Pisces. It demonstrates a virtual marine world inhabited by realistic artificial fishes. Our algorithms emulate not only the appearance, movement, and behavior of individual animals, but also the complex group behaviors evident in many aquatic ecosystems. We model each animal holistically. An artificial fish is an autonomous agent situated in a simulated physical world. The agent has (a) a three-dimensional body with internal muscle actuators and functional fins that deforms and locomotes in accordance with biomechanic and hydrodynamic principles; (b) sensors, including eyes that can image the environment; and (c) a brain with motor, perception, behavior, and learning centers. Artificial fishes exhibit a repertoire of piscatorial behaviors that rely on their perceptual awareness of their dynamic habitat. Individual and emergent collective behaviors include caudal and pectoral locomotion, collision avoidance, foraging, preying, schooling, and mating. Furthermore, artificial fishes can learn how to locomote through practice and sensory reinforcement. Their motor learning algorithms discover muscle controllers that produce efficient hydrodynamic locomotion. The learning algorithms also enable artificial fishes to train themselves to accomplish higher level, perceptually guided motor tasks, such as maneuvering to reach a visible target.

Artificial Fishes: Autonomous Locomotion, Perception, Behavior, and Learning in a Simulated Physical World

This article develops artificial life patterned after animals as evolved as those in the superclass Pisces. It demonstrates a virtual marine world inhabited by realistic artificial fishes. Our algorithms emulate not only the appearance, movement, and behavior of individual animals, but also the complex group behaviors evident in many aquatic ecosystems. We model each animal holistically. An artificial fish is an autonomous agent situated in a simulated physical world. The agent has (a) a three-dimensional body with internal muscle actuators and functional fins that deforms and locomotes in accordance with biomechanic and hydrodynamic principles; (b) sensors, including eyes that can image the environment; and (c) a brain with motor, perception, behavior, and learning centers. Artificial fishes exhibit a repertoire of piscatorial behaviors that rely on their perceptual awareness of their dynamic habitat. Individual and emergent collective behaviors include caudal and pectoral locomotion, collision avoidance, foraging, preying, schooling, and mating. Furthermore, artificial fishes can learn how to locomote through practice and sensory reinforcement. Their motor learning algorithms discover muscle controllers that produce efficient hydrodynamic locomotion. The learning algorithms also enable artificial fishes to train themselves to accomplish higher level, perceptually guided motor tasks, such as maneuvering to reach a visible target.

An Ecological Framework for the Amenities of the City

An ecological study of the city is a new endeavor. Up to now, we have mostly been given inquiries dealing with transportation, housing, economic activity, recreational facilities, etc. All of this adds up to an attempt to reach partial solutions for problems affecting sub-systems. Urbanists and city planners have tried to reach a synthesis of these data whenever they were available.There is an ever increasing need to approach urban problems by borrowing the concepts and the methodology of ecology The origins of this science stem from the research pertaining to plant and animal behavior in their natural habitat. Highly complex adaptation patterns could frequently be observed which allowed a large number of species not only to survive but also to attain a certain level of compatibility with other species. One need not be a determinist to recognize the common basis of adaptation for man and other animals and to tie together the physical and biological needs which underlie more complex individual and group behavior. Thus, for instance, von Frisch and Wilson - have recently redefined the biological bases of the noosphere.