Integer Sparse Distributed Memory and Modular Composite Representation

Challenging AI applications, such as cognitive architectures, natural language understanding, and visual object recognition share some basic operations including pattern recognition, sequence learning, clustering, and association of related data. Both the representations used and the structure of a system significantly influence which tasks and problems are most readily supported. A memory model and a representation that facilitate these basic tasks would greatly improve the performance of these challenging AI applications.Sparse Distributed Memory (SDM), based on large binary vectors, has several desirable properties: auto-associativity, content addressability, distributed storage, robustness over noisy inputs that would facilitate the implementation of challenging AI applications. Here I introduce two variations on the original SDM, the Extended SDM and the Integer SDM, that significantly improve these desirable properties, as well as a new form of reduced description representation named MCR.Extended SDM, which uses word vectors of larger size than address vectors, enhances its hetero-associativity, improving the storage of sequences of vectors, as well as of other data structures. A novel sequence learning mechanism is introduced, and several experiments demonstrate the capacity and sequence learning capability of this memory.Integer SDM uses modular integer vectors rather than binary vectors, improving the representation capabilities of the memory and its noise robustness. Several experiments show its capacity and noise robustness. Theoretical analyses of its capacity and fidelity are also presented.A reduced description represents a whole hierarchy using a single high-dimensional vector, which can recover individual items and directly be used for complex calculations and procedures, such as making analogies. Furthermore, the hierarchy can be reconstructed from the single vector. Modular Composite Representation (MCR), a new reduced description model for the representation used in challenging AI applications, provides an attractive tradeoff between expressiveness and simplicity of operations. A theoretical analysis of its noise robustness, several experiments, and comparisons with similar models are presented.My implementations of these memories include an object oriented version using a RAM cache, a version for distributed and multi-threading execution, and a GPU version for fast vector processing

LIDA: A Systems-level Architecture for Cognition, Emotion, and Learning

Abstract-We describe a cognitive architecture (LIDA) that affords attention, action selection and human-like learning intended for use in controlling cognitive agents that replicate human experiments as well as performing real-world tasks. LIDA combines sophisticated action selection, motivation via emotions, a centrally important attention mechanism, and multimodal instructionalist and selectionist learning. Empirically grounded in cognitive science and cognitive neuroscience, the LIDA architecture employs a variety of modules and processes, each with its own effective representations and algorithms. LIDA has much to say about motivation, emotion, attention, and autonomous learning in cognitive agents. In this paper we summarize the LIDA model together with its resulting agent architecture, describe its computational implementation, and discuss results of simulations that replicate known experimental data. We also discuss some of LIDA's conceptual modules, propose non-linear dynamics as a bridge between LIDA's modules and processes and the underlying neuroscience, and point out some of the differences between LIDA and other cognitive architectures. Finally, we discuss how LIDA addresses some of the open issues in cognitive architecture research

Autenticación Facial Inteligente

Este documento presenta la línea de investigación AURA, dedicada al desarrollo de un framework de codelets combinándolos con redes neuronales para aplicarlo a la autenticación de rostros. Se propone un modelo donde la red neuronal representa el rostro a validar mediante eigenfaces. El resultado es procesado con un framework que permite darle “fluidez” a la validación detectando variaciones sobre la imagen a validar. Este utiliza codelets y una red de conceptos que, en forma descentralizada, permiten darle “significado” a las imágenes que se le presentan. El framework está planteado en forma genérica para aplicarlo a otros dominios en el futuro.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Autenticación Facial Inteligente

Este documento presenta la línea de investigación AURA, dedicada al desarrollo de un framework de codelets combinándolos con redes neuronales para aplicarlo a la autenticación de rostros. Se propone un modelo donde la red neuronal representa el rostro a validar mediante eigenfaces. El resultado es procesado con un framework que permite darle “fluidez” a la validación detectando variaciones sobre la imagen a validar. Este utiliza codelets y una red de conceptos que, en forma descentralizada, permiten darle “significado” a las imágenes que se le presentan. El framework está planteado en forma genérica para aplicarlo a otros dominios en el futuro.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Autenticación Facial Inteligente

Este documento presenta la línea de investigación AURA, dedicada al desarrollo de un framework de codelets combinándolos con redes neuronales para aplicarlo a la autenticación de rostros. Se propone un modelo donde la red neuronal representa el rostro a validar mediante eigenfaces. El resultado es procesado con un framework que permite darle “fluidez” a la validación detectando variaciones sobre la imagen a validar. Este utiliza codelets y una red de conceptos que, en forma descentralizada, permiten darle “significado” a las imágenes que se le presentan. El framework está planteado en forma genérica para aplicarlo a otros dominios en el futuro.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Nonverbal action selection for explanations using an enhanced behavior net

In this paper we present a novel approach to the nonverbal action selection problem for an agent in an intelligent tutoring system. We use a variation of the original Maes\u27 Behavior Net that has several improvements that allow modeling action selection using the content of the utterance, communicative goals, and the discourse history. This Enhanced Behavior Net can perform action selection dynamically, reprioritize actions based on all these elements, and resolve conflict situations without the use of sophisticated predefined rules. © 2011 Springer-Verlag

Artificial General Intelligence7th International Conference, AGI 2014, Quebec City, QC, Canada, August 1-4, 2014. Proceedings /

XII, 268 p. 44 illus.online resource