5 research outputs found
Zero-shot Multi-Domain Dialog State Tracking Using Descriptive Rules
In this work, we present a framework for incorporating descriptive logical rules in state-of-the-art neural networks, enabling them to learn how to handle unseen labels without the introduction of any new training data. The rules are integrated into existing networks without modifying their architecture, through an additional term in the network’s loss function that penalizes states of the network that do not obey the designed rules.As a case of study, the framework is applied to an existing neuralbased Dialog State Tracker. Our experiments demonstrate that the inclusion of logical rules allows the prediction of unseen labels, without deteriorating the predictive capacity of the original system.Fil: Altszyler Lemcovich, Edgar Jaim. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Brusco, Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; ArgentinaFil: Basiou, Nikoletta. Sri International; Estados UnidosFil: Byrnes, John. Sri International; Estados UnidosFil: Vergyri, Dimitra. Sri International; Estados Unido
Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning
Learning the underlying patterns in data goes beyond instance-based
generalization to external knowledge represented in structured graphs or
networks. Deep learning that primarily constitutes neural computing stream in
AI has shown significant advances in probabilistically learning latent patterns
using a multi-layered network of computational nodes (i.e., neurons/hidden
units). Structured knowledge that underlies symbolic computing approaches and
often supports reasoning, has also seen significant growth in recent years, in
the form of broad-based (e.g., DBPedia, Yago) and domain, industry or
application specific knowledge graphs. A common substrate with careful
integration of the two will raise opportunities to develop neuro-symbolic
learning approaches for AI, where conceptual and probabilistic representations
are combined. As the incorporation of external knowledge will aid in
supervising the learning of features for the model, deep infusion of
representational knowledge from knowledge graphs within hidden layers will
further enhance the learning process. Although much work remains, we believe
that knowledge graphs will play an increasing role in developing hybrid
neuro-symbolic intelligent systems (bottom-up deep learning with top-down
symbolic computing) as well as in building explainable AI systems for which
knowledge graphs will provide scaffolding for punctuating neural computing. In
this position paper, we describe our motivation for such a neuro-symbolic
approach and framework that combines knowledge graph and neural networks
KnowSafe: Combined Knowledge and Data Driven Hazard Mitigation in Artificial Pancreas Systems
Significant progress has been made in anomaly detection and run-time
monitoring to improve the safety and security of cyber-physical systems (CPS).
However, less attention has been paid to hazard mitigation. This paper proposes
a combined knowledge and data driven approach, KnowSafe, for the design of
safety engines that can predict and mitigate safety hazards resulting from
safety-critical malicious attacks or accidental faults targeting a CPS
controller. We integrate domain-specific knowledge of safety constraints and
context-specific mitigation actions with machine learning (ML) techniques to
estimate system trajectories in the far and near future, infer potential
hazards, and generate optimal corrective actions to keep the system safe.
Experimental evaluation on two realistic closed-loop testbeds for artificial
pancreas systems (APS) and a real-world clinical trial dataset for diabetes
treatment demonstrates that KnowSafe outperforms the state-of-the-art by
achieving higher accuracy in predicting system state trajectories and potential
hazards, a low false positive rate, and no false negatives. It also maintains
the safe operation of the simulated APS despite faults or attacks without
introducing any new hazards, with a hazard mitigation success rate of 92.8%,
which is at least 76% higher than solely rule-based (50.9%) and data-driven
(52.7%) methods.Comment: 16 pages, 10 figures, 9 tables, submitted to the IEEE for possible
publicatio
Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning
Learning the underlying patterns in data goes beyondinstance-based generalization to external knowledge repre-sented in structured graphs or networks. Deep learning thatprimarily constitutes neural computing stream in AI hasshown significant advances in probabilistically learning la-tent patterns using a multi-layered network of computationalnodes (i.e., neurons/hidden units). Structured knowledge thatunderlies symbolic computing approaches and often supportsreasoning, has also seen significant growth in recent years,in the form of broad-based (e.g., DBPedia, Yago) and do-main, industry or application specific knowledge graphs. Acommon substrate with careful integration of the two willraise opportunities to develop neuro-symbolic learning ap-proaches for AI, where conceptual and probabilistic repre-sentations are combined. As the incorporation of externalknowledge will aid in supervising the learning of features forthe model, deep infusion of representational knowledge fromknowledge graphs within hidden layers will further enhancethe learning process. Although much work remains, we be-lieve that knowledge graphs will play an increasing role in de-veloping hybrid neuro-symbolic intelligent systems (bottom-up deep learning with top-down symbolic computing) as wellas in building explainable AI systems for which knowledgegraphs will provide scaffolding for punctuating neural com-puting. In this position paper, we describe our motivation forsuch a neuro-symbolic approach and framework that com-bines knowledge graph and neural networks
Application of semantic analysis and LSTM-GRU in developing a personalized course recommendation system
The selection of elective courses based on an individual's domain interest is a challenging and critical activity for students at the start of their curriculum. Effective and proper recommendation may result in building a strong expertise in the domain of interest, which in turn improves the outcomes of the students getting better placements, and enrolling into higher studies of their interest, etc. In this paper, an effective course recommendation system is proposed to help the students in facilitating proper course selection based on an individual's domain interest. To achieve this, the core courses in the curriculum are mapped with the predefined domain suggested by the domain experts. These core course contents mapped with the domain are trained semantically using deep learning models to classify the elective courses into domains, and the same are recommended based on the student's domain expertise. The recommendation is validated by analyzing the number of elective course credits completed and the grades scored by a student who utilized the elective course recommendation system, with the grades scored by the student who was subjected to the assessment without elective course recommendations. It was also observed that after the recommendation, the students have registered for a greater number of credits for elective courses on their domain of expertise, which in-turn enables them to have a better learning experience and improved course completion probability.Web of Science1221art. no. 1079