Above are papers related to the following paper: [Paper_ID] Paper #28674. 	[Title] A Theory of Unsupervised Speedup Learning [Abstract] Speedup learning seeks to improve the efficiency of search-based problem solvers. In this paper, we propose a new theoretical model of speedup learning which captures systems that improve problem solving performance by solving a user-given set of problems. We also use this model to motivate the notion of "batch problem solving," and argue that it is more congenial to learning than sequential problem solving. Our theoretical results are applicable to all serially decomposable domains. We empirically validate our results in the domain of Eight Puzzle. 1 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6539. 	[Title] Quantifying Prior Determination Knowledge using the PAC Learning Model [Abstract] Prior knowledge, or bias, regarding a concept can speed up the task of learning it. Probably Approximately Correct (PAC) learning is a mathematical model of concept learning that can be used to quantify the speed up due to different forms of bias on learning. Thus far, PAC learning has mostly been used to analyze syntactic bias, such as limiting concepts to conjunctions of boolean prepositions. This paper demonstrates that PAC learning can also be used to analyze semantic bias, such as a domain theory about the concept being learned. The key idea is to view the hypothesis space in PAC learning as that consistent with all prior knowledge, syntactic and semantic. In particular, the paper presents a PAC analysis of determinations, a type of relevance knowledge. The results of the analysis reveal crisp distinctions and relations among different determinations, and illustrate the usefulness of an analysis based on the PAC model. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #9716. 	[Title] A Formalization of Explanation-Based Macro-operator Learning [Abstract] In spite of the popularity of Explanation-Based Learning (EBL), its theoretical basis is not well-understood. Using a generalization of Probably Approximately Correct (PAC) learning to problem solving domains, this paper formalizes two forms of Explanation-Based Learning of macro-operators and proves the sufficient conditions for their success. These two forms of EBL, called "Macro Caching" and "Serial Parsing," respectively exhibit two distinct sources of power or "bias": the sparseness of the solution space and the decomposability of the problem-space. The analysis shows that exponential speedup can be achieved when either of these biases is suitable for a domain. Somewhat surprisingly, it also shows that computing the preconditions of the macro-operators is not necessary to obtain these speedups. The theoretical results are confirmed by experiments in the domain of Eight Puzzle. Our work suggests that the best way to address the utility problem in EBL is to implement a bias which exploits the problem-space structure of the set of domains that one is interested in learning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153262. 	[Title] Rationality and Intelligence [Abstract] The long-term goal of our field is the creation and understanding of intelligence. Productive research in AI, both practical and theoretical, benefits from a notion of intelligence that is precise enough to allow the cumulative development of robust systems and general results. The concept of rational agency has long been considered a leading candidate to fulfill this role. This paper outlines a gradual evolution in the formal conception of rationality that brings it closer to our informal conception of intelligence and simultaneously reduces the gap between theory and practice. Some directions for future research are indicated. Please summarise all the given information.

Paper #28674 has the title `A Theory of Unsupervised Speedup Learning  
` and the abstract `Speedup learning seeks to improve the efficiency of search-based problem solvers. In this paper, we propose a new theoretical model of speedup learning which captures systems that improve problem solving performance by solving a user-given set of problems. We also use this model to motivate the notion of "batch problem solving," and argue that it is more congenial to learning than sequential problem solving. Our theoretical results are applicable to all serially decomposable domains. We empirically validate our results in the domain of Eight Puzzle. 1 
`.
Question: Which category should Paper #28674 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #28674 has the title `A Theory of Unsupervised Speedup Learning  
` and the abstract `Speedup learning seeks to improve the efficiency of search-based problem solvers. In this paper, we propose a new theoretical model of speedup learning which captures systems that improve problem solving performance by solving a user-given set of problems. We also use this model to motivate the notion of "batch problem solving," and argue that it is more congenial to learning than sequential problem solving. Our theoretical results are applicable to all serially decomposable domains. We empirically validate our results in the domain of Eight Puzzle. 1 
`.
Question: Which category should Paper #28674 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: Theory