Above are papers related to the following paper: [Paper_ID] Paper #1107367. 	[Title] Knowledge Compilation and Speedup Learning in Continuous Task Domains [Abstract] Many techniques for speedup learning and knowledge compilation focus on the learning and optimization of macro-operators or control rules in task domains that can be characterized using a problem-space search paradigm. However, such a characterization does not fit well the class of task domains in which the problem solver is required to perform in a continuous manner. For example, in many robotic domains, the problem solver is required to monitor real-valued perceptual inputs and vary its motor control parameters in a continuous, on-line manner to successfully accomplish its task. In such domains, discrete symbolic states and operators are difficult to define. To improve its performance in continuous problem domains, a problem solver must learn, modify, and use continuous operators that continuously map input sensory information to appropriate control outputs. Additionally, the problem solver must learn the contexts in which those continuous operators are applicable. We propose a learning method that can compile sensorimo-tor experiences into continuous operators, which can then be used to improve performance of the problem solver. The method speeds up the task performance as well as results in improvements in the quality of the resulting solutions. The method is implemented in a robotic navigation system, which is evaluated through extensive experimen tation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6214. 	[Title] Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming [Abstract] This paper extends previous work with Dyna, a class of architectures for intelligent systems based on approximating dynamic programming methods. Dyna architectures integrate trial-and-error (reinforcement) learning and execution-time planning into a single process operating alternately on the world and on a learned model of the world. In this paper, I present and show results for two Dyna architectures. The Dyna-PI architecture is based on dynamic programming's policy iteration method and can be related to existing AI ideas such as evaluation functions and universal plans (reactive systems). Using a navigation task, results are shown for a simple Dyna-PI system that simultaneously learns by trial and error, learns a world model, and plans optimal routes using the evolving world model. The Dyna-Q architecture is based on Watkins's Q-learning, a new kind of reinforcement learning. Dyna-Q uses a less familiar set of data structures than does Dyna-PI, but is arguably simpler to implement and use. We show that Dyna-Q architectures are easy to adapt for use in changing environments. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #22886. 	[Title] Continuous Case-Based Reasoning [Abstract] Case-based reasoning systems have traditionally been used to perform high-level reasoning in problem domains that can be adequately described using discrete, symbolic representations. However, many real-world problem domains, such as autonomous robotic navigation, are better characterized using continuous representations. Such problem domains also require continuous performance, such as online sensorimotor interaction with the environment, and continuous adaptation and learning during the performance task. This article introduces a new method for continuous case-based reasoning, and discusses its application to the dynamic selection, modification, and acquisition of robot behaviors in an autonomous navigation system, SINS (Self-Improving Navigation System). The computer program and the underlying method are systematically evaluated through statistical analysis of results from several empirical studies. The article concludes with a general discussion of case-based reasoning issues addressed by this research. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #73323. 	[Title] A Case-Based Approach to Reactive Control for Autonomous Robots [Abstract] We propose a case-based method of selecting behavior sets as an addition to traditional reactive robotic control systems. The new system (ACBARR | A Case BAsed Reactive Robotic system) provides more flexible performance in novel environments, as well as overcoming a standard "hard" problem for reactive systems, the box canyon. Additionally, ACBARR is designed in a manner which is intended to remain as close to pure reactive control as possible. Higher level reasoning and memory functions are intentionally kept to a minimum. As a result, the new reasoning does not significantly slow the system down from pure reactive speeds. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #194645. 	[Title] Clay: Integrating Motor Schemas and Reinforcement Learning [Abstract] Clay is an evolutionary architecture for autonomous robots that integrates motor schema-based control and reinforcement learning. Robots utilizing Clay benefit from the real-time performance of motor schemas in continuous and dynamic environments while taking advantage of adaptive reinforcement learning. Clay coordinates assemblages (groups of motor schemas) using embedded reinforcement learning modules. The coordination modules activate specific assemblages based on the presently perceived situation. Learning occurs as the robot selects assemblages and samples a reinforcement signal over time. Experiments in a robot soccer simulation illustrate the performance and utility of the system. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #95642. 	[Title] Case-based reactive navigation: A case-based method for on-line selection and adaptation of reactive control parameters [Abstract] This article presents a new line of research investigating on-line learning mechanisms for autonomous intelligent agents. We discuss a case-based method for dynamic selection and modification of behavior assemblages for a navigational system. The case-based reasoning module is designed as an addition to a traditional reactive control system, and provides more flexible performance in novel environments without extensive high-level reasoning that would otherwise slow the system down. The method is implemented in the ACBARR (A Case-BAsed Reactive Robotic) system, and evaluated through empirical simulation of the system on several different environments, including "box canyon" environments known to be problematic for reactive control systems in general. fl Technical Report GIT-CC-92/57, College of Computing, Georgia Institute of Technology, Atlanta, Geor gia, 1992. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #990075. 	[Title] What Kind of Adaptation do CBR Systems Need?: A Review of Current Practice [Abstract] This paper reviews a large number of CBR systems to determine when and what sort of adaptation is currently used. Three taxonomies are proposed: an adaptation-relevant taxonomy of CBR systems, a taxonomy of the tasks performed by CBR systems and a taxonomy of adaptation knowledge. To the extent that the set of existing systems reflects constraints on what is feasible, this review shows interesting dependencies between different system-types, the tasks these systems achieve and the adaptation needed to meet system goals. The CBR system designer may find the partition of CBR systems and the division of adaptation knowledge suggested by this paper useful. Moreover, this paper may help focus the initial stages of systems development by suggesting (on the basis of existing work) what types of adaptation knowledge should be supported by a new system. In addition, the paper provides a framework for the preliminary evaluation and comparison of systems. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1120444. 	[Title] Systematic Evaluation of Design Decisions in CBR Systems [Abstract] Two important goals in the evaluation of an AI theory or model are to assess the merit of the design decisions in the performance of an implemented computer system and to analyze the impact in the performance when the system faces problem domains with different characteristics. This is particularly difficult in case-based reasoning systems because such systems are typically very complex, as are the tasks and domains in which they operate. We present a methodology for the evaluation of case-based reasoning systems through systematic empirical experimentation over a range of system configurations and environmental conditions, coupled with rigorous statistical analysis of the results of the experiments. This methodology enables us to understand the behavior of the system in terms of the theory and design of the computational model, to select the best system configuration for a given domain, and to predict how the system will behave in response to changing domain and problem characteristics. A case study of a mul-tistrategy case-based and reinforcement learning system which performs autonomous robotic navigation is presented as an example. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #28447. 	[Title] MULTISTRATEGY LEARNING IN REACTIVE CONTROL SYSTEMS FOR AUTONOMOUS ROBOTIC NAVIGATION [Abstract] This paper presents a self-improving reactive control system for autonomous robotic navigation. The navigation module uses a schema-based reactive control system to perform the navigation task. The learning module combines case-based reasoning and reinforcement learning to continuously tune the navigation system through experience. The case-based reasoning component perceives and characterizes the system's environment, retrieves an appropriate case, and uses the recommendations of the case to tune the parameters of the reactive control system. The reinforcement learning component refines the content of the cases based on the current experience. Together, the learning components perform on-line adaptation, resulting in improved performance as the reactive control system tunes itself to the environment, as well as on-line case learning, resulting in an improved library of cases that capture environmental regularities necessary to perform on-line adaptation. The system is extensively evaluated through simulation studies using several performance metrics and system configurations. Please summarise all the given information.

Paper #1107367 has the title `Knowledge Compilation and Speedup Learning in Continuous Task Domains  
` and the abstract `Many techniques for speedup learning and knowledge compilation focus on the learning and optimization of macro-operators or control rules in task domains that can be characterized using a problem-space search paradigm. However, such a characterization does not fit well the class of task domains in which the problem solver is required to perform in a continuous manner. For example, in many robotic domains, the problem solver is required to monitor real-valued perceptual inputs and vary its motor control parameters in a continuous, on-line manner to successfully accomplish its task. In such domains, discrete symbolic states and operators are difficult to define. To improve its performance in continuous problem domains, a problem solver must learn, modify, and use continuous operators that continuously map input sensory information to appropriate control outputs. Additionally, the problem solver must learn the contexts in which those continuous operators are applicable. We propose a learning method that can compile sensorimo-tor experiences into continuous operators, which can then be used to improve performance of the problem solver. The method speeds up the task performance as well as results in improvements in the quality of the resulting solutions. The method is implemented in a robotic navigation system, which is evaluated through extensive experimen tation.
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Question: Which category should Paper #1107367 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1107367 has the title `Knowledge Compilation and Speedup Learning in Continuous Task Domains  
` and the abstract `Many techniques for speedup learning and knowledge compilation focus on the learning and optimization of macro-operators or control rules in task domains that can be characterized using a problem-space search paradigm. However, such a characterization does not fit well the class of task domains in which the problem solver is required to perform in a continuous manner. For example, in many robotic domains, the problem solver is required to monitor real-valued perceptual inputs and vary its motor control parameters in a continuous, on-line manner to successfully accomplish its task. In such domains, discrete symbolic states and operators are difficult to define. To improve its performance in continuous problem domains, a problem solver must learn, modify, and use continuous operators that continuously map input sensory information to appropriate control outputs. Additionally, the problem solver must learn the contexts in which those continuous operators are applicable. We propose a learning method that can compile sensorimo-tor experiences into continuous operators, which can then be used to improve performance of the problem solver. The method speeds up the task performance as well as results in improvements in the quality of the resulting solutions. The method is implemented in a robotic navigation system, which is evaluated through extensive experimen tation.
`.
Question: Which category should Paper #1107367 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: Case_Based