Deep Symbolic Learning and Semantics for an Explainable and Ethical Artificial Intelligence

2020 ◽  
pp. 272-278
Author(s):  
Ricardo S. Alonso
Keyword(s):  
Artificial Intelligence ◽  
Symbolic Learning

Chapter 1. Neural-Symbolic Learning and Reasoning: A Survey and Interpretation1

2021 ◽  
Author(s):  
Tarek R. Besold ◽  
Artur d’Avila Garcez ◽  
Sebastian Bader ◽  
Howard Bowman ◽  
Pedro Domingos ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computer Science ◽  
Cognitive Science ◽  
Symbolic Computation ◽  
Software Verification ◽  
Science Research ◽  
Symbolic Learning ◽  
Computer Science Research ◽  
Science Philosophy ◽  
Computational Systems

The study and understanding of human behaviour is relevant to computer science, artificial intelligence, neural computation, cognitive science, philosophy, psychology, and several other areas. Presupposing cognition as basis of behaviour, among the most prominent tools in the modelling of behaviour are computational-logic systems, connectionist models of cognition, and models of uncertainty. Recent studies in cognitive science, artificial intelligence, and psychology have produced a number of cognitive models of reasoning, learning, and language that are underpinned by computation. In addition, efforts in computer science research have led to the development of cognitive computational systems integrating machine learning and automated reasoning. Such systems have shown promise in a range of applications, including computational biology, fault diagnosis, training and assessment in simulators, and software verification. This joint survey reviews the personal ideas and views of several researchers on neural-symbolic learning and reasoning. The article is organised in three parts: Firstly, we frame the scope and goals of neural-symbolic computation and have a look at the theoretical foundations. We then proceed to describe the realisations of neural-symbolic computation, systems, and applications. Finally we present the challenges facing the area and avenues for further research.

scholarly journals Reports of the AAAI 2010 Conference Workshops

AI Magazine ◽  
2010 ◽  
Vol 31 (4) ◽  
pp. 95
Author(s):  
David W. Aha ◽  
Mark Boddy ◽  
Vadim Bulitko ◽  
Artur S. D'Avila Garcez ◽  
Prashant Doshi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Game Theory ◽  
Social Systems ◽  
Visual Representations ◽  
Intent Recognition ◽  
Systems Model ◽  
Symbolic Learning ◽  
Wide Range ◽  
Intelligent Security ◽  
Plan Activity

The AAAI-10 Workshop program was held Sunday and Monday, July 11–12, 2010 at the Westin Peachtree Plaza in Atlanta, Georgia. The AAAI-10 workshop program included 13 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were AI and Fun, Bridging the Gap between Task and Motion Planning, Collaboratively-Built Knowledge Sources and Artificial Intelligence, Goal-Directed Autonomy, Intelligent Security, Interactive Decision Theory and Game Theory, Metacognition for Robust Social Systems, Model Checking and Artificial Intelligence, Neural-Symbolic Learning and Reasoning, Plan, Activity, and Intent Recognition, Statistical Relational AI, Visual Representations and Reasoning, and Abstraction, Reformulation, and Approximation. This article presents short summaries of those events.

Artificial Intelligence

2009 ◽  
Author(s):  
David L. Poole ◽  
Alan K. Mackworth
Keyword(s):  
Artificial Intelligence

Review of Machines Who Think: A Personal Inquiry Into the History and Prospects of Artificial Intelligence.

1981 ◽  
Vol 26 (6) ◽  
pp. 484-484
Author(s):  
George Mandler
Keyword(s):  
Artificial Intelligence

The problems and promises of artificial intelligence

1979 ◽  
Author(s):  
Gregory Freiherr
Keyword(s):  
Artificial Intelligence
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