A survey of induction algorithms for machine learning

Central to all systems for machine learning from examples is an induction algorithm. The purpose of the algorithm is to generalize from a finite set of training examples a description consistent with the examples seen, and, hopefully, with the potentially infinite set of examples not seen. This paper surveys four machine learning induction algorithms. The knowledge representation schemes and a PDL description of algorithm control are emphasized. System characteristics that are peculiar to a domain of application are de-emphasized. Finally, a comparative summary of the learning algorithms is presented

Episodic learning

A system is described which learns to compose sequences of operators into episodes for problem solving. The system incrementally learns when and why operators are applied. Episodes are segmented so that they are generalizable and reusable. The idea of augmenting the instance language with higher level concepts is introduced. The technique of perturbation is described for discovering the essential features for a rule with minimal teacher guidance. The approach is applied to the domain of solving simultaneous linear equations

Machine learning research 1989-90

Multifunctional knowledge bases offer a significant advance in artificial intelligence because they can support numerous expert tasks within a domain. As a result they amortize the costs of building a knowledge base over multiple expert systems and they reduce the brittleness of each system. Due to the inevitable size and complexity of multifunctional knowledge bases, their construction and maintenance require knowledge engineering and acquisition tools that can automatically identify interactions between new and existing knowledge. Furthermore, their use requires software for accessing those portions of the knowledge base that coherently answer questions. Considerable progress was made in developing software for building and accessing multifunctional knowledge bases. A language was developed for representing knowledge, along with software tools for editing and displaying knowledge, a machine learning program for integrating new information into existing knowledge, and a question answering system for accessing the knowledge base

Learning Problem Solving

Learning to problem solve requires acquiring multiple forms of knowledge. Problem solving is viewed as a search of a state-space formulation of a problem. With this formalism, operators are applied to states to transit from the intiial state to the goal state. The learning task is to acquire knowledge of that state-space to guide search. In particular, three forms of knowledge are required: why each operator is useful, when to apply each operator, and what each operator does. A PROLOG implementation, named PET, demonstrates the learning approach in the domains of simultaneous linear equations and symbolic integration.Episodic learning is a technique for learning why individual operators are useful in a solution path. Episodic learning acquires generalized operator sequences which achieve the goal state. This is done by backing-up state evaluation and learning sub-goals in the state-space.Perturbation is a technique for learning when individual operators are useful. Perturbation guides the generalization process to discover minimally-constained preconditions for useful operator applications. This is done by experimentation, thereby reducing the teacher's role in the learning process.Learning relational models is a technique for discovering what individual operators do. Relational models are an explicit representation of the transformation performed by operators. This representation enables the learning element to reason with operator semantics to guide further learning.Episodic learning, perturbation and relational models form an integrated approach for learning problem solving. The approach demonstrates self-teaching by reasoned experimentation

Improving the explanation capabilities of advisory systems

A major limitation of current advisory systems (e.g., intelligent tutoring systems and expert systems) is their restricted ability to give explanations. The goal of our research is to develop and evaluate a flexible explanation facility, one that can dynamically generate responses to questions not anticipated by the system's designers and that can tailor these responses to individual users. To achieve this flexibility, we are developing a large knowledge base, a viewpoint construction facility, and a modeling facility. In the long term we plan to build and evaluate advisory systems with flexible explanation facilities for scientists in numerous domains. In the short term, we are focusing on a single complex domain in biological science, and we are working toward two important milestones: (1) building and evaluating an advisory system with a flexible explanation facility for freshman-level students studying biology, and (2) developing general methods and tools for building similar explanation facilities in other domains