On case-based learnability of languages

2005

CBG-"Casebase Generator" is a decision support system recently developed at Department of Mathematics and Informatics, University of Novi Sad. The technology used for implementing this system is Case-Based Reasoning (CBR). CBR is relatively new and promising area of artificial intelligence where every new problem is solved by adapting the solutions of the previously successfully solved problems. In the past few years CBR has become a popular technique for knowledge-based systems in different domains because the experience is included in solving every new problem. The purpose of this system is to be the main engine of some future, more specialized systems. "Case Base Generator" system is partly described in this paper and some future specialization are analyzed.

1994

This paper focuses on two key issues in building case-based reasoners (CBRs). The first issue is the knowledge engineering phase needed for CBRs as well as knowledge-based systems (KBS); the second issue is the integration of different methods of learning into CBRs. We show that we can use a knowledge modelling framework for the description and implementation of CBR systems; in particular we show how we used it in developing a CBR in the domain of protein purification. In order to encompass CBR (and learning in general) our knowledge modelling framework extends the usual frameworks with the notion of memory. Including memory we provide the capability for storing and retrieving episodes of problem solving, the basis of case-based reasoning and learning. We show here that this framework, and the supporting language NOOS, allows furthermore to integrate other learning methods as needed. Specifically, we show how a method for the induction of class prototypes can be implemented and integrated with case-based methods in an uniform framework.

Humans appear to be able to learn new concepts without needing to be programmed explicitly in any conventional sense. In this paper we regard learning as the phenomenon of knowledge acquisition in the absence of explicit programming. We give a precise methodology for studying this phenomenon from a computational viewpoint. It consists of choosing an appropriate information gathering mechanism, the learning protocol, and exploring the class of concepts that can be learned using it in a reasonable (polynomial) number of steps. Although inherent algorithmic complexity appears to set serious limits to the range of concepts that can be learned, we show that there are some important nontrivial classes of propositional concepts that can be learned in a realistic sense.

IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 2000

Case-Based Decision Theory (CBDT) is a theory of decision making under uncertainty, suggesting that people tend to choose acts that performed well in similar cases they recall. The theory has been developed from a decision-/game-/economic-theoretical point of view, as a potential alternative to expected utility theory (EUT). In this paper, we attempt to reconsider CBDT as a theory of knowledge representation, to contrast it with the rulebased approach, and to study its implications regarding the process of induction.

Artificial Intelligence, 2001

The efficient learnability of restricted classes of logic programs is studied in the PAC framework of computational learning theory. We develop the product homomorphism method, which gives polynomial PAC learning algorithms for a nonrecursive Horn clause with function-free ground background knowledge, if the background knowledge satisfies some structural properties. The method is based on a characterization of the concept that corresponds to the relative least general generalization of a set of positive examples with respect to the background knowledge. The characterization is formulated in terms of products and homomorphisms. In the applications this characterization is turned into an explicit combinatorial description, which is then translated into the language of nonrecursive Horn clauses. We show that a nonrecursive Horn clause is polynomially PAC-learnable if there is a single binary background predicate and the ground atoms in the background knowledge form a forest. If the ground atoms in the background knowledge form a disjoint union of cycles then the situation is different, as the shortest consistent hypothesis may have exponential size. In this case polynomial PAC-learnability holds if a different representation language is used. We also consider the complexity of hypothesis finding for multiple clauses in some restricted cases.

Lecture Notes in Computer Science

Formal language learning models have been widely investigated in the last four decades. But it was not until recently that the model of learning from corrections was introduced. The aim of this paper is to make a further step towards the understanding of the classes of languages learnable with correction queries. We characterize these classes in terms of triples of definite finite telltales. This result allowed us to show that learning with correction queries is strictly more powerful than learning with membership queries, but weaker than the model of learning in the limit from positive data.

Case-based reasoning systems are systems that take advantage from experience, and thus strain after adapting to their environment. They show different ways of achieving this goal. They all share this peculiarity to be memory-driven systems. Thus, adaptability in a casebased reasoning system must be studied from the point of view of the memory. If case-based reasoners generally are devoted to the realization of a single task, the need for such systems to perform various tasks questions how to organize their memory to permit them to be taskadaptive. Moreover they must be able to adapt to several types of cognitive tasks, analysis tasks as well as synthesis tasks. For that purpose, a case-based reasoning system adaptive to the cognitive task is presented in this paper. Its adaptability comes from its memory composition, both cases and concepts, and from its hierarchical memory organization, based on multiple points of view, some of them associated to the various cognitive tasks it performs. For analytic tasks, the most specific cases are preferably used for the reasoning. For synthesis tasks, the most specific concepts, learnt by conceptual clustering, are used. So intensifying the learning inferences during case-based reasoning, and especially the synthetic learning ones, enlarges the application range of case-based reasoning to true synthesis tasks. An example of this system abilities, in the domain of eating disorders in psychiatry, is then briefly presented.

2011

International Journal of Approximate Reasoning, 1988

349 350 Ewa Oflowska 3. Relation concepts (e.g., ordering relation), which apply to relations among objects of some kind (e.g., individuals or sets). 4. Function concepts (e.g., length), which apply to functions of one or more arguments. The two central questions of research on knowledge representation and acquisition in AI systems concern how knowledge is organized and how it can be increased. The construction of AI systems has involved the development of procedures that not only allow the accumulation of facts but also enable us to make intelligent synthesis, that is to aggregate primitive components of knowledge into complex ones.

International Conference on Enterprise Information Systems, 2004

Case Based Learning is an approach to automatic learning and reasoning based on the use of the knowledge gained in past experiences to solve new problems. To suggest a solution for a new problem it is necessary to search for similar problems in the base of problems for which we know their solutions. After selecting one or more similar problems their solutions are used to elaborate a suggested solution for the new problem. Associative memories recover patterns based on their similarity with a new input pattern. This behaviour made them useful to store the base of cases of a Case Based Reasoning system. In this paper we analyze the use of a special model of associative memory named CCLAM (Bailón et al., 2002) with this objective. To test the potentiality of the tool we will discuss its use in a particular application: the detection of the "health" of a company.