Above are papers related to the following paper: [Paper_ID] Paper #56709. 	[Title] GENETIC AND NON GENETIC OPERATORS IN ALECSYS [Abstract] It is well known that standard learning classifier systems, when applied to many different domains, exhibit a number of problems: payoff oscillation, difficult to regulate interplay between the reward system and the background genetic algorithm (GA), rule chains instability, default hierarchies instability, are only a few. ALECSYS is a parallel version of a standard learning classifier system (CS), and as such suffers of these same problems. In this paper we propose some innovative solutions to some of these problems. We introduce the following original features. Mutespec, a new genetic operator used to specialize potentially useful classifiers. Energy, a quantity introduced to measure global convergence in order to apply the genetic algorithm only when the system is close to a steady state. Dynamical adjustment of the classifiers set cardinality, in order to speed up the performance phase of the algorithm. We present simulation results of experiments run in a simulated two-dimensional world in which a simple agent learns to follow a light source. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #28290. 	[Title] Genetics-based Machine Learning and Behaviour Based Robotics: A New Synthesis complexity grows, the learning task [Abstract] difficult. We face this problem using an architecture based on learning classifier systems and on the description of the learning technique used and of the organizational structure proposed, we present experiments that show how behaviour acquisition can be achieved. Our simulated robot learns to structural properties of animal behavioural organization, as proposed by ethologists. After a Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #229635. 	[Title] Evolutionary Neural Networks for Value Ordering in Constraint Satisfaction Problems [Abstract] Technical Report AI94-218 May 1994 Abstract A new method for developing good value-ordering strategies in constraint satisfaction search is presented. Using an evolutionary technique called SANE, in which individual neurons evolve to cooperate and form a neural network, problem-specific knowledge can be discovered that results in better value-ordering decisions than those based on problem-general heuristics. A neural network was evolved in a chronological backtrack search to decide the ordering of cars in a resource-limited assembly line. The network required 1/30 of the backtracks of random ordering and 1/3 of the backtracks of the maximization of future options heuristic. The SANE approach should extend well to other domains where heuristic information is either difficult to discover or problem-specific. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #56708. 	[Title] ALECSYS and the AutonoMouse: Learning to Control a Real Robot by Distributed Classifier Systems [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #83725. 	[Title] Explanations of Empirically Derived Reactive Plans [Abstract] Given an adequate simulation model of the task environment and payoff function that measures the quality of partially successful plans, competition-based heuristics such as genetic algorithms can develop high performance reactive rules for interesting sequential decision tasks. We have previously described an implemented system, called SAMUEL, for learning reactive plans and have shown that the system can successfully learn rules for a laboratory scale tactical problem. In this paper, we describe a method for deriving explanations to justify the success of such empirically derived rule sets. The method consists of inferring plausible subgoals and then explaining how the reactive rules trigger a sequence of actions (i.e., a stra tegy) to satisfy the subgoals. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #18833. 	[Title] Adding Memory to XCS [Abstract] We add internal memory to the XCS classifier system. We then test XCS with internal memory, named XCSM, in non-Markovian environments with two and four aliasing states. Experimental results show that XCSM can easily converge to optimal solutions in simple environments; moreover, XCSM's performance is very stable with respect to the size of the internal memory involved in learning. However, the results we present evidence that in more complex non-Markovian environments, XCSM may fail to evolve an optimal solution. Our results suggest that this happens because, the exploration strategies currently employed with XCS, are not adequate to guarantee the convergence to an optimal policy with XCSM, in complex non-Markovian environments. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #18834. 	[Title] A Study of the Generalization Capabilities of XCS [Abstract] We analyze the generalization behavior of the XCS classifier system in environments in which only a few generalizations can be done. Experimental results presented in the paper evidence that the generalization mechanism of XCS can prevent it from learning even simple tasks in such environments. We present a new operator, named Specify, which contributes to the solution of this problem. XCS with the Specify operator, named XCSS, is compared to XCS in terms of performance and generalization capabilities in different types of environments. Experimental results show that XCSS can deal with a greater variety of environments and that it is more robust than XCS with respect to population size. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1107355. 	[Title] Coordinating Reactive Behaviors  keywords: reactive systems, planning and learning [Abstract] Combinating reactivity with planning has been proposed as a means of compensating for potentially slow response times of planners while still making progress toward long term goals. The demands of rapid response and the complexity of many environments make it difficult to decompose, tune and coordinate reactive behaviors while ensuring consistency. Neural networks can address the tuning problem, but are less useful for decomposition and coordination. We hypothesize that interacting reactions can be decomposed into separate behaviors resident in separate networks and that the interaction can be coordinated through the tuning mechanism and a higher level controller. To explore these issues, we have implemented a neural network architecture as the reactive component of a two layer control system for a simulated race car. By varying the architecture, we test whether decomposing reactivity into separate behaviors leads to superior overall performance, coordination and learning convergence. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #128540. 	[Title] Adapting Control Strategies for Situated Autonomous Agents [Abstract] This paper studies how to balance evolutionary design and human expertise in order to best design situated autonomous agents which can learn specific tasks. A genetic algorithm designs control circuits to learn simple behaviors, and given control strategies for simple behaviors, the genetic algorithm designs a combinational circuit that switches between these simple behaviors to perform a navigation task. Keywords: Genetic Algorithms, Computational Design, Autonomous Agents, Robotics. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #12576. 	[Title] Learning Sequential Decision Rules Using Simulation Models and Competition [Abstract] The problem of learning decision rules for sequential tasks is addressed, focusing on the problem of learning tactical decision rules from a simple flight simulator. The learning method relies on the notion of competition and employs genetic algorithms to search the space of decision policies. Several experiments are presented that address issues arising from differences between the simulation model on which learning occurs and the target environment on which the decision rules are ultimately tested. 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 #8875. 	[Title] A Coevolutionary Approach to Learning Sequential Decision Rules [Abstract] We present a coevolutionary approach to learning sequential decision rules which appears to have a number of advantages over non-coevolutionary approaches. The coevolutionary approach encourages the formation of stable niches representing simpler sub-behaviors. The evolutionary direction of each subbehavior can be controlled independently, providing an alternative to evolving complex behavior using intermediate training steps. Results are presented showing a significant learning rate speedup over a non-coevolutionary approach in a simulated robot domain. In addition, the results suggest the coevolutionary approach may lead to emer gent problem decompositions. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #56112. 	[Title] Simulation-Assisted Learning by Competition: Effects of Noise Differences Between Training Model and Target Environment [Abstract] The problem of learning decision rules for sequential tasks is addressed, focusing on the problem of learning tactical plans from a simple flight simulator where a plane must avoid a missile. The learning method relies on the notion of competition and employs genetic algorithms to search the space of decision policies. Experiments are presented that address issues arising from differences between the simulation model on which learning occurs and the target environment on which the decision rules are ultimately tested. Specifically, either the model or the target environment may contain noise. These experiments examine the effect of learning tactical plans without noise and then testing the plans in a noisy environment, and the effect of learning plans in a noisy simulator and then testing the plans in a noise-free environment. Empirical results show that, while best result are obtained when the training model closely matches the target environment, using a training environment that is more noisy than the target environment is better than using using a training environment that has less noise than the target environment. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #308529. 	[Title] The Role of the Trainer in Reinforcement Learning [Abstract] In this paper we propose a threestage incremental approach to the development of autonomous agents. We discuss some issues about the characteristics which differentiate reinforcement programs (RPs), and define the trainer as a particular kind of RP. We present a set of results obtained running experiments with a trainer which provides guidance to the AutonoMouse, our mousesized autonomous robot. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1152944. 	[Title] Environments with Classifier Systems (Experiments on Adding Memory to XCS) [Abstract] Pier Luca Lanzi Technical Report N. 97.45 October 17 th , 1997 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #575795. 	[Title] Adapting the Evaluation Space to Improve Global Learning [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #56119. 	[Title] Using a Genetic Algorithm to Learn Strategies for Collision Avoidance and Local Navigation [Abstract] Navigation through obstacles such as mine fields is an important capability for autonomous underwater vehicles. One way to produce robust behavior is to perform projective planning. However, real-time performance is a critical requirement in navigation. What is needed for a truly autonomous vehicle are robust reactive rules that perform well in a wide variety of situations, and that also achieve real-time performance. In this work, SAMUEL, a learning system based on genetic algorithms, is used to learn high-performance reactive strategies for navigation and collision avoidance. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #127033. 	[Title] LEARNING ROBOT BEHAVIORS USING GENETIC ALGORITHMS [Abstract] Genetic Algorithms are used to learn navigation and collision avoidance behaviors for robots. The learning is performed under simulation, and the resulting behaviors are then used to control the The approach to learning behaviors for robots described here reflects a particular methodology for learning via a simulation model. The motivation is that making mistakes on real systems may be costly or dangerous. In addition, time constraints might limit the number of experiences during learning in the real world, while in many cases, the simulation model can be made to run faster than real time. Since learning may require experimenting with behaviors that might occasionally produce unacceptable results if applied to the real world, or might require too much time in the real environment, we assume that hypothetical behaviors will be evaluated in a simulation model (the off-line system). As illustrated in Figure 1, the current best behavior can be placed in the real, on-line system, while learning continues in the off-line system [1]. The learning algorithm was designed to learn useful behaviors from simulations of limited fidelity. The expectation is that behaviors learned in these simulations will be useful in real-world environments. Previous studies have illustrated that knowledge learned under simulation is robust and might be applicable to the real world if the simulation is more general (i.e. has more noise, more varied conditions, etc.) than the real world environment [2]. Where this is not possible, it is important to identify the differences between the simulation and the world and note the effect upon the learning process. The research reported here continues to examine this hypothesis. The next section very briefly explains the learning algorithm (and gives pointers to where more extensive documentation can be found). After that, the actual robot is described. Then we describe the simulation of the robot. The task _______________ actual robot. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6213. 	[Title] Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction, [Abstract] This article introduces a class of incremental learning procedures specialized for prediction|that is, for using past experience with an incompletely known system to predict its future behavior. Whereas conventional prediction-learning methods assign credit by means of the difference between predicted and actual outcomes, the new methods assign credit by means of the difference between temporally successive predictions. Although such temporal-difference methods have been used in Samuel's checker player, Holland's bucket brigade, and the author's Adaptive Heuristic Critic, they have remained poorly understood. Here we prove their convergence and optimality for special cases and relate them to supervised-learning methods. For most real-world prediction problems, temporal-difference methods require less memory and less peak computation than conventional methods; and they produce more accurate predictions. We argue that most problems to which supervised learning is currently applied are really prediction problems of the sort to which temporal-difference methods can be applied to advantage. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1105221. 	[Title] ADAPTIVE TESTING OF CONTROLLERS FOR AUTONOMOUS VEHICLES [Abstract] Autonomous vehicles are likely to require sophisticated software controllers to maintain vehicle performance in the presence of vehicle faults. The test and evaluation of complex software controllers is expected to be a challenging task. The goal of this e ffort is to apply machine learning techniques from the field of arti ficial intelligence to the general problem of evaluating an intelligent controller for an autonomous vehicle. The approach involves subjecting a controller to an adaptively chosen set of fault scenarios within a vehicle simulator, and searching for combinations of faults that produce noteworthy performance by the vehicle controller. The search employs a genetic algorithm. We illustrate the approach by evaluating the performance of a subsumption-based controller for an autonomous vehicle. The preliminary evidence suggests that this approach is an e ffective alternative to manual testing of sophisticated software controllers. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #416455. 	[Title] ROBO-SHEPHERD: LEARNING COMPLEX ROBOTIC BEHAVIORS [Abstract] This paper reports on recent results using genetic algorithms to learn decision rules for complex robot behaviors. The method involves evaluating hypothetical rule sets on a simulator and applying simulated evolution to evolve more effective rules. The main contributions of this paper are (1) the task learned is a complex behavior involving multiple mobile robots, and (2) the learned rules are verified through experiments on operational mobile robots. The case study involves a shepherding task in which one mobile robot attempts to guide another robot to a specified area. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #387795. 	[Title] University of Nevada Reno Design Strategies for Evolutionary Robotics [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #86359. 	[Title] Evolving Optimal Populations with XCS Classifier Systems [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #63832. 	[Title] An Evolutionary Approach to Learning in Robots [Abstract] Evolutionary learning methods have been found to be useful in several areas in the development of intelligent robots. In the approach described here, evolutionary algorithms are used to explore alternative robot behaviors within a simulation model as a way of reducing the overall knowledge engineering effort. This paper presents some initial results of applying the SAMUEL genetic learning system to a collision avoidance and navigation task for mobile robots. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #135130. 	[Title] USING A GENETIC ALGORITHM TO LEARN BEHAVIORS FOR AUTONOMOUS VEHICLES [Abstract] Truly autonomous vehicles will require both projec - tive planning and reactive components in order to perform robustly. Projective components are needed for long-term planning and replanning where explicit reasoning about future states is required. Reactive components allow the system to always have some action available in real-time, and themselves can exhibit robust behavior, but lack the ability to expli - citly reason about future states over a long time period. This work addresses the problem of creating reactive components for autonomous vehicles. Creating reactive behaviors (stimulus-response rules) is generally difficult, requiring the acquisition of much knowledge from domain experts, a problem referred to as the knowledge acquisition bottleneck. SAMUEL is a system that learns reactive behaviors for autonomous agents. SAMUEL learns these behaviors under simulation, automating the process of creating stimulus-response rules and therefore reducing the bottleneck. The learning algorithm was designed to learn useful behaviors from simulations of limited fidelity. Current work is investigating how well behaviors learned under simulation environments work in real world environments. In this paper, we describe SAMUEL, and describe behaviors that have been learned for simulated autonomous aircraft, autonomous underwater vehicles, and robots. These behaviors include dog fighting, missile evasion, track - ing, navigation, and obstacle avoidance. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1107674. 	[Title] EVOLUTIONARY ALGORITHMS IN ROBOTICS [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #567005. 	[Title] An unsupervised neural network for low-level control of a wheeled mobile robot: noise resistance, stability, [Abstract] We have recently introduced a neural network mobile robot controller (NETMORC) that autonomously learns the forward and inverse odometry of a differential drive robot through an unsupervised learning-by-doing cycle. After an initial learning phase, the controller can move the robot to an arbitrary stationary or moving target while compensating for noise and other forms of disturbance, such as wheel slippage or changes in the robot's plant. In addition, the forward odometric map allows the robot to reach targets in the absence of sensory feedback. The controller is also able to adapt in response to long-term changes in the robot's plant, such as a change in the radius of the wheels. In this article we review the NETMORC architecture and describe its simplified algorithmic implementation, we present new, quantitative results on NETMORC's performance and adaptability under noise-free and noisy conditions, we compare NETMORC's performance on a trajectory-following task with the performance of an alternative controller, and we describe preliminary results on the hardware implementation of NETMORC with the mobile robot ROBUTER. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1152740. 	[Title] Model of the Environment to Avoid Local Learning [Abstract] Pier Luca Lanzi Technical Report N. 97.46 December 20 th , 1997 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1129573. 	[Title] The Exploitation of Cooperation in Iterated Prisoner's Dilemma [Abstract] We follow Axelrod [2] in using the genetic algorithm to play Iterated Prisoner's Dilemma. Each member of the population (i.e., each strategy) is evaluated by how it performs against the other members of the current population. This creates a dynamic environment in which the algorithm is optimising to a moving target instead of the usual evaluation against some fixed set of strategies, causing an "arms race" of innovation [3]. We conduct two sets of experiments. The first set investigates what conditions evolve the best strategies. The second set studies the robustness of the strategies thus evolved, that is, are the strategies useful only in the round robin of its population or are they effective against a wide variety of opponents? Our results indicate that the population has nearly always converged by about 250 generations, by which time the bias in the population has almost always stabilised at 85%. Our results confirm that cooperation almost always becomes the dominant strategy [1, 2]. We can also confirm that seeding the population with expert strategies is best done in small amounts so as to leave the initial population with plenty of genetic diversity [7]. The lack of robustness in strategies produced in the round robin evaluation is demonstrated by some examples of a population of nave cooperators being exploited by a defect-first strategy. This causes a sudden but ephemeral decline in the population's average score, but it recovers when less nave cooperators emerge and do well against the exploiting strategies. This example of runaway evolution is brought back to reality by a suitable mutation, reminiscent of punctuated equilibria [12]. We find that a way to reduce such navity is to make the GA population play against an extra, Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1104999. 	[Title] EVOLVING ROBOT BEHAVIORS [Abstract] This paper discusses the use of evolutionary computation to evolve behaviors that exhibit emergent intelligent behavior. Genetic algorithms are used to learn navigation and collision avoidance behaviors for robots. The learning is performed under simulation, and the resulting behaviors are then used to control the actual robot. Some of the emergent behavior is described in detail. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #4584. 	[Title] References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages [Abstract] Miller, G. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. The Psychological Review, 63(2):81-97. Schmidhuber, J. (1990b). Towards compositional learning with dynamic neural networks. Technical Report FKI-129-90, Technische Universitat Munchen, Institut fu Informatik. Servan-Schreiber, D., Cleermans, A., and McClelland, J. (1988). Encoding sequential structure in simple recurrent networks. Technical Report CMU-CS-88-183, Carnegie Mellon University, Computer Science Department. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1385. 	[Title] REINFORCEMENT LEARNING FOR COORDINATED REACTIVE CONTROL [Abstract] The demands of rapid response and the complexity of many environments make it difficult to decompose, tune and coordinate reactive behaviors while ensuring consistency. Reinforcement learning networks can address the tuning problem, but do not address the problem of decomposition and coordination. We hypothesize that interacting reactions can often be decomposed into separate control tasks resident in separate networks and that the interaction can be coordinated through the tuning mechanism and a higher level controller. To explore these issues, we have implemented a reinforcement learning architecture as the reactive component of a two layer control system for a simulated race car. By varying the architecture, we test whether decomposing reactivity into separate controllers leads to superior overall performance and learning convergence in our domain. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1071981. 	[Title] Learning to Sense Selectively in Physical Domains [Abstract] In this paper we describe an approach to representing, using, and improving sensory skills for physical domains. We present Icarus, an architecture that represents control knowledge in terms of durative states and sequences of such states. The system operates in cycles, activating a state that matches the environmental situation and letting that state control behavior until its conditions fail or until finding another matching state with higher priority. Information about the probability that conditions will remain satisfied minimizes demands on sensing, as does knowledge about the durations of states and their likely successors. Three statistical learning methods let the system gradually reduce sensory load as it gains experience in a domain. We report experimental evaluations of this ability on three simulated physical tasks: flying an aircraft, steering a truck, and balancing a pole. Our experiments include lesion studies that identify the reduction in sensing due to each of the learning mechanisms and others that examine the effect of domain characteristics. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #28287. 	[Title] Robot Shaping: Developing Situated Agents through Learning [Abstract] Learning plays a vital role in the development of situated agents. In this paper, we explore the use of reinforcement learning to "shape" a robot to perform a predefined target behavior. We connect both simulated and real robots to A LECSYS, a parallel implementation of a learning classifier system with an extended genetic algorithm. After classifying different kinds of Animat-like behaviors, we explore the effects on learning of different types of agent's architecture (monolithic, flat and hierarchical) and of training strategies. In particular, hierarchical architecture requires the agent to learn how to coordinate basic learned responses. We show that the best results are achieved when both the agent's architecture and the training strategy match the structure of the behavior pattern to be learned. We report the results of a number of experiments carried out both in simulated and in real environments, and show that the results of simulations carry smoothly to real robots. While most of our experiments deal with simple reactive behavior, in one of them we demonstrate the use of a simple and general memory mechanism. As a whole, our experimental activity demonstrates that classifier systems with genetic algorithms can be practically employed to develop autonomous agents. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #114. 	[Title] Learning to Act using Real-Time Dynamic Programming [Abstract] fl The authors thank Rich Yee, Vijay Gullapalli, Brian Pinette, and Jonathan Bachrach for helping to clarify the relationships between heuristic search and control. We thank Rich Sutton, Chris Watkins, Paul Werbos, and Ron Williams for sharing their fundamental insights into this subject through numerous discussions, and we further thank Rich Sutton for first making us aware of Korf's research and for his very thoughtful comments on the manuscript. We are very grateful to Dimitri Bertsekas and Steven Sullivan for independently pointing out an error in an earlier version of this article. Finally, we thank Harry Klopf, whose insight and persistence encouraged our interest in this class of learning problems. This research was supported by grants to A.G. Barto from the National Science Foundation (ECS-8912623 and ECS-9214866) and the Air Force Office of Scientific Research, Bolling AFB (AFOSR-89-0526). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106547. 	[Title] Learning to Race: Experiments with a Simulated Race Car [Abstract] We have implemented a reinforcement learning architecture as the reactive component of a two layer control system for a simulated race car. We have found that separating the layers has expedited gradually improving competition and mult-agent interaction. We ran experiments to test the tuning, decomposition and coordination of the low level behaviors. We then extended our control system to allow passing of other cars and tested its ability to avoid collisions. The best design used reinforcement learning with separate networks for each behavior, coarse coded input and a simple rule based coordination mechanism. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #574710. 	[Title] ROBOT LEARNING WITH PARALLEL GENETIC ALGORITHMS ON NETWORKED COMPUTERS [Abstract] This work explores the use of machine learning methods for extracting knowledge from simulations of complex systems. In particular, we use genetic algorithms to learn rule-based strategies used by autonomous robots. The evaluation of a given strategy may require several executions of a simulation to produce a meaningful estimate of the quality of the strategy. As a consequence, the evaluation of a single individual in the genetic algorithm requires a fairly substantial amount of computation. Such a system suggests the sort of large-grained parallelism that is available on a network of workstations. We describe an implementation of a parallel genetic algorithm, and present case studies of the resulting speedup on two robot learning tasks. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #887. 	[Title] Some studies in machine learning using the game of checkers. IBM Journal, 3(3):211-229, 1959. Some [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #62718. 	[Title] AN ENHANCER FOR REACTIVE PLANS [Abstract] This paper describes our method for improving the comprehensibility, accuracy, and generality of reactive plans. A reactive plan is a set of reactive rules. Our method involves two phases: (1) formulate explanations of execution traces, and then (2) generate new reactive rules from the explanations. Since the explanation phase has been previously described, the primary focus of this paper is the rule generation phase. This latter phase consists of taking a subset of the explanations and using these explanations to generate a set of new reactive rules to add to the original set. The particular subset of the explanations that is chosen yields rules that provide new domain knowledge for handling knowledge gaps in the original rule set. The original rule set, in a complimentary manner, provides expertise to fill the gaps where the domain knowledge provided by the new rules is incomplete. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128959. 	[Title] On the Relations Between Search and Evolutionary Algorithms [Abstract] Technical Report: CSRP-96-7 March 1996 Abstract Evolutionary algorithms are powerful techniques for optimisation whose operation principles are inspired by natural selection and genetics. In this paper we discuss the relation between evolutionary techniques, numerical and classical search methods and we show that all these methods are instances of a single more general search strategy, which we call the `evolutionary computation cookbook'. By combining the features of classical and evolutionary methods in different ways new instances of this general strategy can be generated, i.e. new evolutionary (or classical) algorithms can be designed. One such algorithm, GA fl , is described. Please summarise all the given information.

Paper #56709 has the title `GENETIC AND NON GENETIC OPERATORS IN ALECSYS  
` and the abstract `It is well known that standard learning classifier systems, when applied to many different domains, exhibit a number of problems: payoff oscillation, difficult to regulate interplay between the reward system and the background genetic algorithm (GA), rule chains instability, default hierarchies instability, are only a few. ALECSYS is a parallel version of a standard learning classifier system (CS), and as such suffers of these same problems. In this paper we propose some innovative solutions to some of these problems. We introduce the following original features. Mutespec, a new genetic operator used to specialize potentially useful classifiers. Energy, a quantity introduced to measure global convergence in order to apply the genetic algorithm only when the system is close to a steady state. Dynamical adjustment of the classifiers set cardinality, in order to speed up the performance phase of the algorithm. We present simulation results of experiments run in a simulated two-dimensional world in which a simple agent learns to follow a light source. 
`.
Question: Which category should Paper #56709 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #56709 has the title `GENETIC AND NON GENETIC OPERATORS IN ALECSYS  
` and the abstract `It is well known that standard learning classifier systems, when applied to many different domains, exhibit a number of problems: payoff oscillation, difficult to regulate interplay between the reward system and the background genetic algorithm (GA), rule chains instability, default hierarchies instability, are only a few. ALECSYS is a parallel version of a standard learning classifier system (CS), and as such suffers of these same problems. In this paper we propose some innovative solutions to some of these problems. We introduce the following original features. Mutespec, a new genetic operator used to specialize potentially useful classifiers. Energy, a quantity introduced to measure global convergence in order to apply the genetic algorithm only when the system is close to a steady state. Dynamical adjustment of the classifiers set cardinality, in order to speed up the performance phase of the algorithm. We present simulation results of experiments run in a simulated two-dimensional world in which a simple agent learns to follow a light source. 
`.
Question: Which category should Paper #56709 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