40,517 research outputs found
A robust semantics hides fewer errors
In this paper we explore how formal models are interpreted and to what degree meaning is captured in the formal semantics and to what degree it remains in the informal interpretation of the semantics. By applying a robust approach to the definition of refinement and semantics, favoured by the event-based community, to state-based theory we are able to move some aspects from the informal interpretation into the formal semantics
End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosis
Beyond current conversational chatbots or task-oriented dialogue systems that
have attracted increasing attention, we move forward to develop a dialogue
system for automatic medical diagnosis that converses with patients to collect
additional symptoms beyond their self-reports and automatically makes a
diagnosis. Besides the challenges for conversational dialogue systems (e.g.
topic transition coherency and question understanding), automatic medical
diagnosis further poses more critical requirements for the dialogue rationality
in the context of medical knowledge and symptom-disease relations. Existing
dialogue systems (Madotto, Wu, and Fung 2018; Wei et al. 2018; Li et al. 2017)
mostly rely on data-driven learning and cannot be able to encode extra expert
knowledge graph. In this work, we propose an End-to-End Knowledge-routed
Relational Dialogue System (KR-DS) that seamlessly incorporates rich medical
knowledge graph into the topic transition in dialogue management, and makes it
cooperative with natural language understanding and natural language
generation. A novel Knowledge-routed Deep Q-network (KR-DQN) is introduced to
manage topic transitions, which integrates a relational refinement branch for
encoding relations among different symptoms and symptom-disease pairs, and a
knowledge-routed graph branch for topic decision-making. Extensive experiments
on a public medical dialogue dataset show our KR-DS significantly beats
state-of-the-art methods (by more than 8% in diagnosis accuracy). We further
show the superiority of our KR-DS on a newly collected medical dialogue system
dataset, which is more challenging retaining original self-reports and
conversational data between patients and doctors.Comment: 8 pages, 5 figues, AAA
New directions for Artificial Intelligence (AI) methods in optimum design
Developments and applications of artificial intelligence (AI) methods in the design of structural systems is reviewed. Principal shortcomings in the current approach are emphasized, and the need for some degree of formalism in the development environment for such design tools is underscored. Emphasis is placed on efforts to integrate algorithmic computations in expert systems
Schema Independent Relational Learning
Learning novel concepts and relations from relational databases is an
important problem with many applications in database systems and machine
learning. Relational learning algorithms learn the definition of a new relation
in terms of existing relations in the database. Nevertheless, the same data set
may be represented under different schemas for various reasons, such as
efficiency, data quality, and usability. Unfortunately, the output of current
relational learning algorithms tends to vary quite substantially over the
choice of schema, both in terms of learning accuracy and efficiency. This
variation complicates their off-the-shelf application. In this paper, we
introduce and formalize the property of schema independence of relational
learning algorithms, and study both the theoretical and empirical dependence of
existing algorithms on the common class of (de) composition schema
transformations. We study both sample-based learning algorithms, which learn
from sets of labeled examples, and query-based algorithms, which learn by
asking queries to an oracle. We prove that current relational learning
algorithms are generally not schema independent. For query-based learning
algorithms we show that the (de) composition transformations influence their
query complexity. We propose Castor, a sample-based relational learning
algorithm that achieves schema independence by leveraging data dependencies. We
support the theoretical results with an empirical study that demonstrates the
schema dependence/independence of several algorithms on existing benchmark and
real-world datasets under (de) compositions
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