Above are papers related to the following paper: [Paper_ID] Paper #9708. 	[Title] Acquiring Recursive and Iterative Concepts with Explanation-Based Learning explanation-based generalization, generalizing explanation structures, generalizing to [Abstract] University of Wisconsin Computer Sciences Technical Report 876 (September 1989) Abstract In explanation-based learning, a specific problem's solution is generalized into a form that can be later used to solve conceptually similar problems. Most research in explanation-based learning involves relaxing constraints on the variables in the explanation of a specific example, rather than generalizing the graphical structure of the explanation itself. However, this precludes the acquisition of concepts where an iterative or recursive process is implicitly represented in the explanation by a fixed number of applications. This paper presents an algorithm that generalizes explanation structures and reports empirical results that demonstrate the value of acquiring recursive and iterative concepts. The BAGGER2 algorithm learns recursive and iterative concepts, integrates results from multiple examples, and extracts useful subconcepts during generalization. On problems where learning a recursive rule is not appropriate, the system produces the same result as standard explanation-based methods. Applying the learned recursive rules only requires a minor extension to a PROLOG-like problem solver, namely, the ability to explicitly call a specific rule. Empirical studies demonstrate that generalizing the structure of explanations helps avoid the recently reported negative effects of learning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #270600. 	[Title] GENERALIZATION BY CONTROLLED EXPANSION OF EXAMPLES [Abstract] SG (Specific to General) is a learning system that derives general rules from specific examples. SG learns incrementally with good speed and generalization. The SG network is built of many simple nodes that adapt to the problem being learned. Learning is done without requiring user adjustment of sensitive parameters and noise is tolerated with graceful degradation in performance. Nodes learn important features in the input space and then monitor the ability of the features to predict output values. Learning is O(n log n) for each example, where n is the number of nodes in the network, and the number of inputs and output values are treated as constants. An enhanced network topology reduces time complexity to O(log n). Empirical results show that the model gives good generalization and that learning converges in a small number of training passes. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153160. 	[Title] Inductive Logic Programming [Abstract] A new research area, Inductive Logic Programming, is presently emerging. While inheriting various positive characteristics of the parent subjects of Logic Programming and Machine Learning, it is hoped that the new area will overcome many of the limitations of its forebears. The background to present developments within this area is discussed and various goals and aspirations for the increasing body of researchers are identified. Inductive Logic Programming needs to be based on sound principles from both Logic and Statistics. On the side of statistical justification of hypotheses we discuss the possible relationship between Algorithmic Complexity theory and Probably-Approximately-Correct (PAC) Learning. In terms of logic we provide a unifying framework for Muggleton and Buntine's Inverse Resolution (IR) and Plotkin's Relative Least General Generali-sation (RLGG) by rederiving RLGG in terms of IR. This leads to a discussion of the feasibility of extending the RLGG framework to allow for the invention of new predicates, previously discussed only within the context of IR. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #2440. 	[Title] Quinlan, 1990 J.R. Quinlan. Learning logical definitions from relations. Machine Learning, First-order theory revision. In [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 #1130637. 	[Title] Observation and Generalisation in a Simulated Robot World [Abstract] This paper describes a program which observes the behaviour of actors in a simulated world and uses these observations as guides to conducting experiments. An experiment is a sequence of actions carried out by an actor in order to support or weaken the case for a generalisation of a concept. A generalisation is attempted when the program observes a state of the world which is similar to a some previous state. A partial matching algorithm is used to find substitutions which enable the two states to be unified. The generalisation of the two states is their unifier. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #348437. 	[Title] Learning First-Order Acyclic Horn Programs from Entailment [Abstract] In this paper, we consider learning first-order Horn programs from entailment. In particular, we show that any subclass of first-order acyclic Horn programs with constant arity is exactly learnable from equivalence and entailment membership queries provided it allows a polynomial-time subsumption procedure and satisfies some closure conditions. One consequence of this is that first-order acyclic determinate Horn programs with constant arity are exactly learnable from equiv alence and entailment membership queries. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #181782. 	[Title] Hierarchical Explanation-Based Reinforcement Learning [Abstract] Explanation-Based Reinforcement Learning (EBRL) was introduced by Dietterich and Flann as a way of combining the ability of Reinforcement Learning (RL) to learn optimal plans with the generalization ability of Explanation-Based Learning (EBL) (Di-etterich & Flann, 1995). We extend this work to domains where the agent must order and achieve a sequence of subgoals in an optimal fashion. Hierarchical EBRL can effectively learn optimal policies in some of these sequential task domains even when the subgoals weakly interact with each other. We also show that when a planner that can achieve the individual subgoals is available, our method converges even faster. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1130539. 	[Title] Learning High Utility Rules by Incorporating Search Control  Guidance Committee [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #58540. 	[Title] Machine Learning,  Explanation-Based Learning and Reinforcement Learning: A Unified View [Abstract] In speedup-learning problems, where full descriptions of operators are known, both explanation-based learning (EBL) and reinforcement learning (RL) methods can be applied. This paper shows that both methods involve fundamentally the same process of propagating information backward from the goal toward the starting state. Most RL methods perform this propagation on a state-by-state basis, while EBL methods compute the weakest preconditions of operators, and hence, perform this propagation on a region-by-region basis. Barto, Bradtke, and Singh (1995) have observed that many algorithms for reinforcement learning can be viewed as asynchronous dynamic programming. Based on this observation, this paper shows how to develop dynamic programming versions of EBL, which we call region-based dynamic programming or Explanation-Based Reinforcement Learning (EBRL). The paper compares batch and online versions of EBRL to batch and online versions of point-based dynamic programming and to standard EBL. The results show that region-based dynamic programming combines the strengths of EBL (fast learning and the ability to scale to large state spaces) with the strengths of reinforcement learning algorithms (learning of optimal policies). Results are shown in chess endgames and in synthetic maze tasks. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #2354. 	[Title] A Statistical Approach to Solving the EBL Utility Problem [Abstract] Many "learning from experience" systems use information extracted from problem solving experiences to modify a performance element PE, forming a new element PE 0 that can solve these and similar problems more efficiently. However, as transformations that improve performance on one set of problems can degrade performance on other sets, the new PE 0 is not always better than the original PE; this depends on the distribution of problems. We therefore seek the performance element whose expected performance, over this distribution, is optimal. Unfortunately, the actual distribution, which is needed to determine which element is optimal, is usually not known. Moreover, the task of finding the optimal element, even knowing the distribution, is intractable for most interesting spaces of elements. This paper presents a method, palo, that side-steps these problems by using a set of samples to estimate the unknown distribution, and by using a set of transformations to hill-climb to a local optimum. This process is based on a mathematically rigorous form of utility analysis: in particular, it uses statistical techniques to determine whether the result of a proposed transformation will be better than the original system. We also present an efficient way of implementing this learning system in the context of a general class of performance elements, and include empirical evidence that this approach can work effectively. fl Much of this work was performed at the University of Toronto, where it was supported by the Institute for Robotics and Intelligent Systems and by an operating grant from the National Science and Engineering Research Council of Canada. We also gratefully acknowledge receiving many helpful comments from William Cohen, Dave Mitchell, Dale Schuurmans and the anonymous referees. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #63931. 	[Title] Combining FOIL and EBG to Speed-up Logic Programs [Abstract] This paper presents an algorithm that combines traditional EBL techniques and recent developments in inductive logic programming to learn effective clause selection rules for Prolog programs. When these control rules are incorporated into the original program, significant speed-up may be achieved. The algorithm is shown to be an improvement over competing EBL approaches in several domains. Additionally, the algorithm is capable of automatically transforming some intractable algorithms into ones that run in polynomial time. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #136766. 	[Title] Learning Goal-Decomposition Rules using Exercises [Abstract] Exercises are problems ordered in increasing order of difficulty. Teaching problem-solving through exercises is a widely used pedagogic technique. A computational reason for this is that the knowledge gained by solving simple problems is useful in efficiently solving more difficult problems. We adopt this approach of learning from exercises to acquire search-control knowledge in the form of goal-decomposition rules (d-rules). D-rules are first order, and are learned using a new "generalize-and-test" algorithm which is based on inductive logic programming techniques. We demonstrate the feasibility of the approach by applying it in two planning do mains. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #136767. 	[Title] Learning Horn Definitions with Equivalence and Membership Queries [Abstract] A Horn definition is a set of Horn clauses with the same head literal. In this paper, we consider learning non-recursive, function-free first-order Horn definitions. We show that this class is exactly learnable from equivalence and membership queries. It follows then that this class is PAC learnable using examples and membership queries. Our results have been shown to be applicable to learning efficient goal-decomposition rules in planning domains. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #136768. 	[Title] Theory-guided Empirical Speedup Learning of Goal Decomposition Rules [Abstract] Speedup learning is the study of improving the problem-solving performance with experience and from outside guidance. We describe here a system that successfully combines the best features of Explanation-based learning and empirical learning to learn goal decomposition rules from examples of successful problem solving and membership queries. We demonstrate that our system can efficiently learn effective decomposition rules in three different domains. Our results suggest that theory-guided empirical learning can overcome the problems of purely explanation-based learning and purely empirical learning, and be an effective speedup learning method. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6209. 	[Title] Transfer of Learning by Composing Solutions of Elemental Sequential Tasks [Abstract] Although building sophisticated learning agents that operate in complex environments will require learning to perform multiple tasks, most applications of reinforcement learning have focussed on single tasks. In this paper I consider a class of sequential decision tasks (SDTs), called composite sequential decision tasks, formed by temporally concatenating a number of elemental sequential decision tasks. Elemental SDTs cannot be decomposed into simpler SDTs. I consider a learning agent that has to learn to solve a set of elemental and composite SDTs. I assume that the structure of the composite tasks is unknown to the learning agent. The straightforward application of reinforcement learning to multiple tasks requires learning the tasks separately, which can waste computational resources, both memory and time. I present a new learning algorithm and a modular architecture that learns the decomposition of composite SDTs, and achieves transfer of learning by sharing the solutions of elemental SDTs across multiple composite SDTs. The solution of a composite SDT is constructed by computationally inexpensive modifications of the solutions of its constituent elemental SDTs. I provide a proof of one aspect of the learning algorithm. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #51909. 	[Title] Automated Refinement of First-Order Horn-Clause Domain Theories [Abstract] Knowledge acquisition is a difficult, error-prone, and time-consuming task. The task of automatically improving an existing knowledge base using learning methods is addressed by the class of systems performing theory refinement. This paper presents a system, Forte (First-Order Revision of Theories from Examples), which refines first-order Horn-clause theories by integrating a variety of different revision techniques into a coherent whole. Forte uses these techniques within a hill-climbing framework, guided by a global heuristic. It identifies possible errors in the theory and calls on a library of operators to develop possible revisions. The best revision is implemented, and the process repeats until no further revisions are possible. Operators are drawn from a variety of sources, including propositional theory refinement, first-order induction, and inverse resolution. Forte is demonstrated in several domains, including logic programming and qualitative modelling. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #34257. 	[Title] A Self-Adjusting Dynamic Logic Module [Abstract] This paper presents an ASOCS (Adaptive Self-Organizing Concurrent System) model for massively parallel processing of incrementally defined rule systems in such areas as adaptive logic, robotics, logical inference, and dynamic control. An ASOCS is an adaptive network composed of many simple computing elements operating asynchronously and in parallel. This paper focuses on Adaptive Algorithm 2 (AA2) and details its architecture and learning algorithm. AA2 has significant memory and knowledge maintenance advantages over previous ASOCS models. An ASOCS can operate in either a data processing mode or a learning mode. During learning mode, the ASOCS is given a new rule expressed as a boolean conjunction. The AA2 learning algorithm incorporates the new rule in a distributed fashion in a short, bounded time. During data processing mode, the ASOCS acts as a parallel hardware circuit. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #411092. 	[Title] LEARNING LOGICAL EXCEPTIONS IN CHESS [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6741. 	[Title] LEARNING CONCEPTS BY ASKING QUESTIONS [Abstract] Tw o important issues in machine learning are explored: the role that memory plays in acquiring new concepts; and the extent to which the learner can take an active part in acquiring these concepts. This chapter describes a program, called Marvin, which uses concepts it has learned previously to learn new concepts. The program forms hypotheses about the concept being learned and tests the hypotheses by asking the trainer questions. Learning begins when the trainer shows Marvin an example of the concept to be learned. The program determines which objects in the example belong to concepts stored in the memory. A description of the new concept is formed by using the information obtained from the memory to generalize the description of the training example. The generalized description is tested when the program constructs new examples and shows these to the trainer, asking if they belong to the target concept. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #143323. 	[Title] AN EMPIRICAL APPROACH TO SOLVING THE GENERAL UTILITY PROBLEM IN SPEEDUP LEARNING [Abstract] The utility problem in speedup learning describes a common behavior of machine learning methods: the eventual degradation of performance due to increasing amounts of learned knowledge. The shape of the learning curve (cost of using a learning method vs. number of training examples) over several domains suggests a parameterized model relating performance to the amount of learned knowledge and a mechanism to limit the amount of learned knowledge for optimal performance. Many recent approaches to avoiding the utility problem in speedup learning rely on sophisticated utility measures and significant numbers of training data to accurately estimate the utility of control knowledge. Empirical results presented here and elsewhere indicate that a simple selection strategy of retaining all control rules derived from a training problem explanation quickly defines an efficient set of control knowledge from few training problems. This simple selection strategy provides a low-cost alternative to example-intensive approaches for improving the speed of a problem solver. Experimentation illustrates the existence of a minimum (representing least cost) in the learning curve which is reached after a few training examples. Stress is placed on controlling the amount of learned knowledge as opposed to which knowledge. An attempt is also made to relate domain characteristics to the shape of the learning curve. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #368605. 	[Title] A Generalizing Adaptive Discriminant Network [Abstract] This paper overviews the AA1 (Adaptive Algorithm 1) model of ASOCS the (Adaptive Self - Organizing Concurrent Systems) approach. It also presents promising empirical generalization results of AA1 with actual data. AA1 is a topologically dynamic network which grows to fit the problem being learned. AA1 generalizes in a self-organizing fashion to a network which seeks to find features which discriminate between concepts. Convergence to a training set is both guaranteed and bounded linearly in time. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #49895. 	[Title] The Origins of Inductive Logic Programming: A Prehistoric Tale [Abstract] This paper traces the development of the main ideas that have led to the present state of knowledge in Inductive Logic Programming. The story begins with research in psychology on the subject of human concept learning. Results from this research influenced early efforts in Artificial Intelligence which combined with the formal methods of inductive inference to evolve into the present discipline of Inductive Logic Programming. Inductive Logic Programming is often considered to be a young discipline. However, it has its roots in research dating back nearly 40 years. This paper traces the development of ideas beginning in psychology and the effect they had on concept learning research in Artificial Intelligence. Independent of any requirement for a psychological basis, formal methods of inductive inference were developed. These separate streams eventually gave rise to Inductive Logic Programming. This account is not entirely unbiased. More attention is given to the work of those researchers who most influenced my own interest in machine learning. Being a retrospective paper, I do not attempt to describe recent developments in ILP. This account only includes research prior to 1991 the year in which the term Inductive Logic Programming was first used (Muggleton, 1991). This is the reason for the subtitle A Prehistoric Tale. The major headings in the paper are taken from the names of periods in the evolution of life on Earth. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #37483. 	[Title] Learning Approximate Control Rules Of High Utility [Abstract] One of the difficult problems in the area of explanation based learning is the utility problem; learning too many rules of low utility can lead to swamping, or degradation of performance. This paper introduces two new techniques for improving the utility of learned rules. The first technique is to combine EBL with inductive learning techniques to learn a better set of control rules; the second technique is to use these inductive techniques to learn approximate control rules. The two techniques are synthesized in an algorithm called approximating abductive explanation based learning (AxA-EBL). AxA-EBL is shown to improve substantially over standard EBL in several domains. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153003. 	[Title] Eclectic Machine Learning [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #417017. 	[Title] Relating Relational Learning Algorithms [Abstract] Relational learning algorithms are of special interest to members of the machine learning community; they offer practical methods for extending the representations used in algorithms that solve supervised learning tasks. Five approaches are currently being explored to address issues involved with using relational representations. This paper surveys algorithms embodying these approaches, summarizes their empirical evaluations, highlights their commonalities, and suggests potential directions for future research. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #198653. 	[Title] Information filtering: Selection mechanisms in learning systems. Machine Learning, 10:113-151, 1993. Generalization as search. Artificial [Abstract]  Please summarise all the given information.

Paper #9708 has the title `Acquiring Recursive and Iterative Concepts with Explanation-Based Learning explanation-based generalization, generalizing explanation structures, generalizing to
` and the abstract `University of Wisconsin Computer Sciences Technical Report 876 (September 1989) Abstract In explanation-based learning, a specific problem's solution is generalized into a form that can be later used to solve conceptually similar problems. Most research in explanation-based learning involves relaxing constraints on the variables in the explanation of a specific example, rather than generalizing the graphical structure of the explanation itself. However, this precludes the acquisition of concepts where an iterative or recursive process is implicitly represented in the explanation by a fixed number of applications. This paper presents an algorithm that generalizes explanation structures and reports empirical results that demonstrate the value of acquiring recursive and iterative concepts. The BAGGER2 algorithm learns recursive and iterative concepts, integrates results from multiple examples, and extracts useful subconcepts during generalization. On problems where learning a recursive rule is not appropriate, the system produces the same result as standard explanation-based methods. Applying the learned recursive rules only requires a minor extension to a PROLOG-like problem solver, namely, the ability to explicitly call a specific rule. Empirical studies demonstrate that generalizing the structure of explanations helps avoid the recently reported negative effects of learning. 
`.
Question: Which category should Paper #9708 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #9708 has the title `Acquiring Recursive and Iterative Concepts with Explanation-Based Learning explanation-based generalization, generalizing explanation structures, generalizing to
` and the abstract `University of Wisconsin Computer Sciences Technical Report 876 (September 1989) Abstract In explanation-based learning, a specific problem's solution is generalized into a form that can be later used to solve conceptually similar problems. Most research in explanation-based learning involves relaxing constraints on the variables in the explanation of a specific example, rather than generalizing the graphical structure of the explanation itself. However, this precludes the acquisition of concepts where an iterative or recursive process is implicitly represented in the explanation by a fixed number of applications. This paper presents an algorithm that generalizes explanation structures and reports empirical results that demonstrate the value of acquiring recursive and iterative concepts. The BAGGER2 algorithm learns recursive and iterative concepts, integrates results from multiple examples, and extracts useful subconcepts during generalization. On problems where learning a recursive rule is not appropriate, the system produces the same result as standard explanation-based methods. Applying the learned recursive rules only requires a minor extension to a PROLOG-like problem solver, namely, the ability to explicitly call a specific rule. Empirical studies demonstrate that generalizing the structure of explanations helps avoid the recently reported negative effects of learning. 
`.
Question: Which category should Paper #9708 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