59,144 research outputs found
Terrorism Event Classification Using Fuzzy Inference Systems
Terrorism has led to many problems in Thai societies, not only property
damage but also civilian casualties. Predicting terrorism activities in advance
can help prepare and manage risk from sabotage by these activities. This paper
proposes a framework focusing on event classification in terrorism domain using
fuzzy inference systems (FISs). Each FIS is a decision-making model combining
fuzzy logic and approximate reasoning. It is generated in five main parts: the
input interface, the fuzzification interface, knowledge base unit, decision
making unit and output defuzzification interface. Adaptive neuro-fuzzy
inference system (ANFIS) is a FIS model adapted by combining the fuzzy logic
and neural network. The ANFIS utilizes automatic identification of fuzzy logic
rules and adjustment of membership function (MF). Moreover, neural network can
directly learn from data set to construct fuzzy logic rules and MF implemented
in various applications. FIS settings are evaluated based on two comparisons.
The first evaluation is the comparison between unstructured and structured
events using the same FIS setting. The second comparison is the model settings
between FIS and ANFIS for classifying structured events. The data set consists
of news articles related to terrorism events in three southern provinces of
Thailand. The experimental results show that the classification performance of
the FIS resulting from structured events achieves satisfactory accuracy and is
better than the unstructured events. In addition, the classification of
structured events using ANFIS gives higher performance than the events using
only FIS in the prediction of terrorism events.Comment: IEEE Publication format, ISSN 1947 5500,
http://sites.google.com/site/ijcsis
The 1990 progress report and future plans
This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers
Building Machines That Learn and Think Like People
Recent progress in artificial intelligence (AI) has renewed interest in
building systems that learn and think like people. Many advances have come from
using deep neural networks trained end-to-end in tasks such as object
recognition, video games, and board games, achieving performance that equals or
even beats humans in some respects. Despite their biological inspiration and
performance achievements, these systems differ from human intelligence in
crucial ways. We review progress in cognitive science suggesting that truly
human-like learning and thinking machines will have to reach beyond current
engineering trends in both what they learn, and how they learn it.
Specifically, we argue that these machines should (a) build causal models of
the world that support explanation and understanding, rather than merely
solving pattern recognition problems; (b) ground learning in intuitive theories
of physics and psychology, to support and enrich the knowledge that is learned;
and (c) harness compositionality and learning-to-learn to rapidly acquire and
generalize knowledge to new tasks and situations. We suggest concrete
challenges and promising routes towards these goals that can combine the
strengths of recent neural network advances with more structured cognitive
models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary
proposals (until Nov. 22, 2016).
https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar
- …