Rule-based systems must solve complex matching problems within tight time constraints to be effective in real-time applications, such as planning and reactive control for AI agents, as well as low-latency relational database querying. Pattern-matching systems can encounter issues where exponential time and space are required to find matches for rules with many underconstrained variables, or which produce combinatorial intermediate partial matches (but are otherwise well-constrained). When online AI systems automatically generate rules from example-driven induction or code synthesis, they can easily produce worst-case matching patterns that slow or halt program execution by exceeding available memory. In our own work with cognitive systems that learn from example, we've found that aggressive forms of anti-unification-based generalization can easily produce these circumstances. To make these systems practical without hand-engineering constraints or succumbing to unpredictable failure modes, we introduce a new matching algorithm called CORGI (Collection-Oriented Relational Graph Iteration). Unlike RETE-based approaches, CORGI offers quadratic time and space guarantees for finding single satisficing matches, and the ability to iteratively stream subsequent matches without committing entire conflict sets to memory. CORGI differs from RETE in that it does not have a traditional $β$-memory for collecting partial matches. Instead, CORGI takes a two-step approach: a graph of grounded relations is built/maintained in a forward pass, and an iterator generates matches as needed by working backward through the graph. This approach eliminates the high-latency delays and memory overflows that can result from populating full conflict sets. In a performance evaluation, we demonstrate that CORGI significantly outperforms RETE implementations from SOAR and OPS5 on a simple combinatorial matching task.

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Personality is considered one of the most influential research topics in psychology, as it predicts many consequential outcomes such as mental and physical health and explains human behaviour. With the widespread use of social networks as a means of communication, it is becoming increasingly important to develop models that can automatically and accurately read the essence of individuals based solely on their writing. In particular, the convergence of social and computer sciences has led researchers to develop automatic approaches for extracting and studying "hidden" information in textual data on the internet. The nature of this thesis project is highly experimental, and the motivation behind this work is to present detailed analyses on the topic, as currently there are no significant investigations of this kind. The objective is to identify an adequate semantic space that allows for defining the personality of the object to which a certain text refers. The starting point is a dictionary of adjectives that psychological literature defines as markers of the five major personality traits, or Big Five. In this work, we started with the implementation of fully-connected neural networks as a basis for understanding how simple deep learning models can provide information on hidden personality characteristics. Finally, we use a class of distributional algorithms invented in 2013 by Tomas Mikolov, which consists of using a convolutional neural network that learns the contexts of words in an unsupervised way. In this way, we construct an embedding that contains the semantic information on the text, obtaining a kind of "geometry of meaning" in which concepts are translated into linear relationships. With this last experiment, we hypothesize that an individual writing style is largely coupled with their personality traits.

Efficient indexing schemes have influenced the acceptance of production systems in the industrial world. However, in embedded-control systems, production systems have not been applied intensively because of their nondeterministic run-time behavior. Thus, nonpredictability of response times is a major obstacle to the widespread use of expert systems in the real-time domain. Such systems are considered intelligent when they are able to perform complex actions in response to the sensed environment. In intelligent real-time systems, there is a tradeoff between acting and reasoning.

The Rete Match Algorithm is an efficient method for comparing a large collection of patterns to a large collection of objects. It finds all the objects that match each pattern. The algorithm was developed for use in production system interpreters, and it has been used for systems containing from a few hundred to more than a thousand patterns and objects. This article presents the algorithm in detail. It explains the basic concepts of the algorithm, it describes pattern and object representations that are appropriate for the algorithm, and it describes the operations performed by the pattern matcher.