Above are papers related to the following paper: [Paper_ID] Paper #144212. 	[Title] Evolving nonTrivial Behaviors on Real Robots: an Autonomous Robot that Picks up Objects [Abstract] Recently, a new approach that involves a form of simulated evolution has been proposed for the building of autonomous robots. However, it is still not clear if this approach may be adequate to face real life problems. In this paper we show how control systems that perform a nontrivial sequence of behaviors can be obtained with this methodology by carefully designing the conditions in which the evolutionary process operates. In the experiment described in the paper, a mobile robot is trained to locate, recognize, and grasp a target object. The controller of the robot has been evolved in simulation and then downloaded and tested on the real robot. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1033. 	[Title] The Evolutionary Cost of Learning [Abstract] Traits that are acquired by members of an evolving population during their lifetime, through adaptive processes such as learning, can become genetically specified in later generations. Thus there is a change in the level of learning in the population over evolutionary time. This paper explores the idea that as well as the benefits to be gained from learning, there may also be costs to be paid for the ability to learn. It is these costs that supply the selection pressure for the genetic assimilation of acquired traits. Two models are presented that attempt to illustrate this assertion. The first uses Kauffman's NK fitness landscapes to show the effect that both explicit and implicit costs have on the assimilation of learnt traits. A characteristic `hump' is observed in the graph of the level of plasticity in the population showing that learning is first selected for and then against as evolution progresses. The second model is a practical example in which neural network controllers are evolved for a small mobile robot. Results from this experiment also show the hump. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1035. 	[Title] EVOLVING SENSORS IN ENVIRONMENTS OF CONTROLLED COMPLEXITY [Abstract] 1 . Sensors represent a crucial link between the evolutionary forces shaping a species' relationship with its environment, and the individual's cognitive abilities to behave and learn. We report on experiments using a new class of "latent energy environments" (LEE) models to define environments of carefully controlled complexity which allow us to state bounds for random and optimal behaviors that are independent of strategies for achieving the behaviors. Using LEE's analytic basis for defining environments, we then use neural networks (NNets) to model individuals and a steady - state genetic algorithm to model an evolutionary process shaping the NNets, in particular their sensors. Our experiments consider two types of "contact" and "ambient" sensors, and variants where the NNets are not allowed to learn, learn via error correction from internal prediction, and via reinforcement learning. We find that predictive learning, even when using a larger repertoire of the more sophisticated ambient sensors, provides no advantage over NNets unable to learn. However, reinforcement learning using a small number of crude contact sensors does provide a significant advantage. Our analysis of these results points to a tradeoff between the genetic "robustness" of sensors and their informativeness to a learning system. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #61069. 	[Title] Tracking the red queen: Measurements of adaptive progress in co-evolution ary simulations. In Third European [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #240791. 	[Title] Auto-teaching: networks that develop their own teaching input [Abstract] Backpropagation learning (Rumelhart, Hinton and Williams, 1986) is a useful research tool but it has a number of undesiderable features such as having the experimenter decide from outside what should be learned. We describe a number of simulations of neural networks that internally generate their own teaching input. The networks generate the teaching input by trasforming the network input through connection weights that are evolved using a form of genetic algorithm. What results is an innate (evolved) capacity not to behave efficiently in an environment but to learn to behave efficiently. The analysis of what these networks evolve to learn shows some interesting results. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #568857. 	[Title] Evolving Self-Supporting Structures Page 18 References Evolution of Visual Control Systems for Robots. To appear [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1131549. 	[Title] An Artificial Life Model for Investigating the Evolution of Modularity [Abstract] To investigate the issue of how modularity emerges in nature, we present an Artificial Life model that allow us to reproduce on the computer both the organisms (i.e., robots that have a genotype, a nervous system, and sensory and motor organs) and the environment in which organisms live, behave and reproduce. In our simulations neural networks are evolutionarily trained to control a mobile robot designed to keep an arena clear by picking up trash objects and releasing them outside the arena. During the evolutionary process modular neural networks, which control the robot's behavior, emerge as a result of genetic duplications. Preliminary simulation results show that duplication-based modular architecture outperforms the nonmod-ular architecture, which represents the starting architecture in our simulations. Moreover, an interaction between mutation and duplication rate emerges from our results. Our future goal is to use this model in order to explore the relationship between the evolutionary emergence of modularity and the phenomenon of gene duplication. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #35. 	[Title] 4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley. [Abstract] To implement a specific application, you should only have to change the file app.c. Section 2 describes the routines in app.c in detail. If you use additional variables for your specific problem, the easiest method of making them available to other program units is to declare them in sga.h and external.h. However, take care that you do not redeclare existing variables. Two example applications files are included in the SGA-C distribution. The file app1.c performs the simple example problem included with the Pascal version; finding the maximum of x 10 , where x is an integer interpretation of a chromosome. A slightly more complex application is include in app2.c. This application illustrates two features that have been added to SGA-C. The first of these is the ithruj2int function, which converts bits i through j in a chromosome to an integer. The second new feature is the utility pointer that is associated with each population member. The example application interprets each chromosome as a set of concatenated integers in binary form. The lengths of these integer fields is determined by the user-specified value of field size, which is read in by the function app data(). The field size must be less than the smallest of the chromosome length and the length of an unsigned integer. An integer array for storing the interpreted form of each chromosome is dynamically allocated and assigned to the chromosome's utility pointer in app malloc(). The ithruj2int routine (see utility.c) is used to translate each chromosome into its associated vector. The fitness for each chromosome is simply the sum of the squares of these integers. This example application will function for any chromosome length. SGA-C is intended to be a simple program for first-time GA experimentation. It is not intended to be definitive in terms of its efficiency or the grace of its implementation. The authors are interested in the comments, criticisms, and bug reports from SGA-C users, so that the code can be refined for easier use in subsequent versions. Please email your comments to rob@galab2.mh.ua.edu, or write to TCGA: The authors gratefully acknowledge support provided by NASA under Grant NGT-50224 and support provided by the National Science Foundation under Grant CTS-8451610. We also thank Hillol Kargupta for donating his tournament selection implementation. Booker, L. B. (1982). Intelligent behavior as an adaptation to the task environment (Doctoral dissertation, Technical Report No. 243. Ann Arbor: University of Michigan, Logic of Computers Group). Dissertations Abstracts International, 43(2), 469B. (University Microfilms No. 8214966) Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #45605. 	[Title] Learning and evolution in neural networks [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #44455. 	[Title] Evolving Visually Guided Robots [Abstract] A version of this paper appears in: Proceedings of SAB92, the Second International Conference on Simulation of Adaptive Behaviour J.-A. Meyer, H. Roitblat, and S. Wilson, editors, MIT Press Bradford Books, Cambridge, MA, 1993. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #650807. 	[Title] Discontinuity in evolution: how different levels of organization imply pre-adaptation [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #197054. 	[Title] Evolving Obstacle Avoidance Behavior in a Robot Arm [Abstract] Existing approaches for learning to control a robot arm rely on supervised methods where correct behavior is explicitly given. It is difficult to learn to avoid obstacles using such methods, however, because examples of obstacle avoidance behavior are hard to generate. This paper presents an alternative approach that evolves neural network controllers through genetic algorithms. No input/output examples are necessary, since neuro-evolution learns from a single performance measurement over the entire task of grasping an object. The approach is tested in a simulation of the OSCAR-6 robot arm which receives both visual and sensory input. Neural networks evolved to effectively avoid obstacles at various locations to reach random target locations. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #682815. 	[Title] The Role of Development in Genetic Algorithms [Abstract] Technical Report Number CS94-394 Computer Science and Engineering, U.C.S.D. Abstract The developmental mechanisms transforming genotypic to phenotypic forms are typically omitted in formulations of genetic algorithms (GAs) in which these two representational spaces are identical. We argue that a careful analysis of developmental mechanisms is useful when understanding the success of several standard GA techniques, and can clarify the relationships between more recently proposed enhancements. We provide a framework which distinguishes between two developmental mechanisms | learning and maturation | while also showing several common effects on GA search. This framework is used to analyze how maturation and local search can change the dynamics of the GA. We observe that in some contexts, maturation and local search can be incorporated into the fitness evaluation, but illustrate reasons for considering them seperately. Further, we identify contexts in which maturation and local search can be distinguished from the fitness evaluation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #503871. 	[Title] Growing neural networks [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #182094. 	[Title] HOW TO EVOLVE AUTONOMOUS ROBOTS: DIFFERENT APPROACHES IN EVOLUTIONARY ROBOTICS [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #975567. 	[Title] Adaptive Behavior in Competing Co-Evolving Species [Abstract] Co-evolution of competitive species provides an interesting testbed to study the role of adaptive behavior because it provides unpredictable and dynamic environments. In this paper we experimentally investigate some arguments for the co-evolution of different adaptive protean behaviors in competing species of predators and preys. Both species are implemented as simulated mobile robots (Kheperas) with infrared proximity sensors, but the predator has an additional vision module whereas the prey has a maximum speed set to twice that of the predator. Different types of variability during life for neurocontrollers with the same architecture and genetic length are compared. It is shown that simple forms of pro-teanism affect co-evolutionary dynamics and that preys rather exploit noisy controllers to generate random trajectories, whereas predators benefit from directional-change controllers to improve pursuit behavior. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #32083. 	[Title] Back Propagation is Sensitive to Initial Conditions [Abstract] This paper explores the effect of initial weight selection on feed-forward networks learning simple functions with the back-propagation technique. We first demonstrate, through the use of Monte Carlo techniques, that the magnitude of the initial condition vector (in weight space) is a very significant parameter in convergence time variability. In order to further understand this result, additional deterministic experiments were performed. The results of these experiments demonstrate the extreme sensitivity of back propagation to initial weight configuration. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #190697. 	[Title] Analysis of Neurocontrollers Designed by Simulated Evolution [Abstract] Randomized, adaptive, greedy search using evolutionary algorithms offers a powerful and versatile approach to the automated design of neural network architectures for a variety of tasks in artificial intelligence and robotics. In this paper we present results from the evolutionary design of a neuro-controller for a robotic bulldozer. This robot is given the task of clearing an arena littered with boxes by pushing boxes to the sides. Through a careful analysis of the evolved networks we show how evolution exploits the design constraints and properties of the environment to produce network structures of high fitness. We conclude with a brief summary of related ongoing research examining the intricate interplay between environment and evolutionary processes in determining the structure and function of the resulting neural architectures. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #41714. 	[Title] Issues in Evolutionary Robotics [Abstract] A version of this paper appears in: Proceedings of SAB92, the Second International Conference on Simulation of Adaptive Behaviour J.-A. Meyer, H. Roitblat, and S. Wilson, editors, MIT Press Bradford Books, Cambridge, MA, 1993. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1110515. 	[Title] Environmental Effects on Minimal Behaviors in the Minimat World [Abstract] The structure of an environment affects the behaviors of the organisms that have evolved in it. How is that structure to be described, and how can its behavioral consequences be explained and predicted? We aim to establish initial answers to these questions by simulating the evolution of very simple organisms in simple environments with different structures. Our artificial creatures, called "minimats," have neither sensors nor memory and behave solely by picking amongst the actions of moving, eating, reproducing, and sitting, according to an inherited probability distribution. Our simulated environments contain only food (and multiple minimats) and are structured in terms of their spatial and temporal food density and the patchiness with which the food appears. Changes in these environmental parameters affect the evolved behaviors of minimats in different ways, and all three parameters are of importance in describing the minimat world. One of the most useful behavioral strategies that evolves is "looping" movement, which allows minimats-despite their lack of internal state-to match their behavior to the temporal (and spatial) structure of their environment. Ultimately we find that minimats construct their own environments through their individual behaviors, making the study of the impact of global environment structure on individual behavior much more complex. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #190706. 	[Title] Selection for Wandering Behavior in a Small Robot [Abstract] We have evolved artificial neural networks to control the wandering behavior of small robots. The task was to touch as many squares in a grid as possible during a fixed period of time. A number of the simulated robots were embodied in small Lego (Trademark) robot, controlled by a Motorola (Trademark) 6811 processor; and their performance was compared to the simulations. We observed that: (a) evolution was an effective means to program control; (b) progress was characterized by sharply stepped periods of improvement, separated by periods of stasis that corresponded to levels of behavioral/computational complexity; and (c) the simulated and realized robots behaved quite similarly, the realized robots in some cases outperforming the simulated ones. Introducing random noise to the simulations improved the fit somewhat (from 0.73 to 0.79). Hybrid simulated/embodied selection regimes for evolutionary robots are discussed. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #35061. 	[Title] Evolving Networks: Using the Genetic Algorithm with Connectionist Learning [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1131639. 	[Title] Language as a dynamical system [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #594047. 	[Title] A Hybrid GP/GA Approach for Co-evolving Controllers and Robot Bodies to Achieve Fitness-Specified Tasks [Abstract] Evolutionary approaches have been advocated to automate robot design. Some research work has shown the success of evolving controllers for the robots by genetic approaches. As we can observe, however, not only the controller but also the robot body itself can affect the behavior of the robot in a robot system. In this paper, we develop a hybrid GP/GA approach to evolve both controllers and robot bodies to achieve behavior-specified tasks. In order to assess the performance of the developed approach, it is used to evolve a simulated agent, with its own controller and body, to do obstacle avoidance in the simulated environment. Experimental results show the promise of this work. In addition, the importance of co-evolving controllers and robot bodies is analyzed and discussed in this paper. Please summarise all the given information.

Paper #144212 has the title `Evolving nonTrivial Behaviors on Real Robots: an Autonomous Robot that Picks up Objects  
` and the abstract `Recently, a new approach that involves a form of simulated evolution has been proposed for the building of autonomous robots. However, it is still not clear if this approach may be adequate to face real life problems. In this paper we show how control systems that perform a nontrivial sequence of behaviors can be obtained with this methodology by carefully designing the conditions in which the evolutionary process operates. In the experiment described in the paper, a mobile robot is trained to locate, recognize, and grasp a target object. The controller of the robot has been evolved in simulation and then downloaded and tested on the real robot. 
`.
Question: Which category should Paper #144212 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #144212 has the title `Evolving nonTrivial Behaviors on Real Robots: an Autonomous Robot that Picks up Objects  
` and the abstract `Recently, a new approach that involves a form of simulated evolution has been proposed for the building of autonomous robots. However, it is still not clear if this approach may be adequate to face real life problems. In this paper we show how control systems that perform a nontrivial sequence of behaviors can be obtained with this methodology by carefully designing the conditions in which the evolutionary process operates. In the experiment described in the paper, a mobile robot is trained to locate, recognize, and grasp a target object. The controller of the robot has been evolved in simulation and then downloaded and tested on the real robot. 
`.
Question: Which category should Paper #144212 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Genetic_Algorithms