Style transformation refers to the process by which a piece of text written in a certain style of writing is transformed into another text exhibiting a distinctly different style of writing without significant change to the meaning of individual sentences. In this paper we continue investigation into the linguistic style transformation problem and demonstrate current achievements in transformation on sample texts from a standard authorship attribution corpus. Specifically, we use simple synonym and phrase replacement on the source text to strengthen the stylistic markers of a given target corpus. We validate our results using Java Graphical Authorship Attribution Program (JGAAP). We are able to demonstrate that simple replacements can alter the linguistic style of writing as detected by an independent process.

Pattern‐weight pairs (PWs) are a new form of search and planning knowledge. PWs are predicates over states coupled with a least upper bound on the distance from any state satisfying that predicate to any goal state. The relationship of PWs to more traditional forms of search knowledge is explored with emphasis on macros and subgoals. It is shown how PWs may be used to overcome some of the difficulties associated with macro‐tables and lead to shorter solution paths without replanning. An algorithm is given for converting a macro‐table to a more powerful PW set. Superiority over the Squeeze algorithm is demonstrated. It is also shown how PWs provide a mechanism for achieving dynamic subgoaling through the combination of knowledge from multiple alternative subgoal sequences. The flexibility and execution time reasoning provided by PWs may have significant use in reactive domains. The main cost associated with PWs is the cost of applying them at execution time. An associative retrieval algorithm is given that expedites this matching‐evaluation process. Empirical results are provided which demonstrate asymptotically improving performance with problem size of the PW technique over macro‐tables.

This paper provides a blueprint for the development of a fully domain‐independent single‐agent and multiagent heuristic search system. It gives a graph‐theoretic representation of search problems based on conceptual graphs and outlines two different learning systems. One, an “informed learner”, makes use of the graph‐theoretic definition of a search problem or game in playing and adapting to a game in the given environment. The other, a “blind learner”, is not given access to the rules of a domain but must discover and then exploit the underlying mathematical structure of a given domain. Relevant work of others is referenced within the context of the blueprint. To illustrate further how one might go about creating general game‐playing agents, we show how we can generalize the understanding obtained with the Morph chess system to all games involving the interactions of abstract mathematical relations. A monitor for such domains has been developed, along with an implementation of a blind and informed learning system known as Morphll. Performance results with MorphK are preliminary but encouraging and provide a few more data points with which to understand and evaluate the blueprint.

predictive search (APS) is a new method by which systems can improve their search performance through experience. It is believed that the development of such methods is critical, as currently a tremendous number of computational results are potentially wasted by not integrating search and partial search results into the knowledge of a problem-solving system. In the APS model, pattern formation and associative recall are used to improve or replace search. In this paper, the theory, background, and motivations behind the model are presented and its application to two-player game-playing programs is discussed. In these programs, the system develops a knowledge base of patterns (boolean features) coupled with weights and, using pattern-oriented evaluation, performs only 1-ply search, yet competes respectably with programs that search more. The learning mechanism is a hybrid of machine learning and artificial intelligence techniques that have been successful in other settings. Specific examples and performance results are taken from the domains of Hexapawn, Tic-Tac-Toe, Pente, Othello, and chess.

This article is an overview of research on the design and application of a Self-Organizing Pattern Retrieval System. This system is a nontraditional form of database for several reasons: (1) the objects (patterns) stored in the database are complex data structures such as graphs, or matrices as opposed to relations, sets, lists or frames; (2) the system attempts to optimize its internal structure for retrieval efficiency by taking advantage of classificatory information (common patterns) it discovers about the patterns in the database; and (3) the system is representation-independent, allowing the user to use whatever representation scheme he desires as long as he provides a small set of I/O routines and comparison functions for the representation. This system has been applied to the organization of molecules and reactions in organic chemistry. the system is able to select valid precursors to molecules based on its knowledge of real-world reactions. It is also able to suggest new chemical reactions based on generalizations from its knowledge base. the system has also been applied to the retrieval, generalization, and classification of chess positions. Its ability to recognize the tactical similarity of positions is compared with that of a chess master. the design objectives of a system for classifying Radio Frequency Interference required by NASA's SETI (Search for ExtraTerrestrial Intelligence) project is also discussed.