scholarly journals Short-Circuiting the Definition of Mathematical Knowledge for an Artificial General Intelligence

2021 ◽  
pp. 201-213
Author(s):  
Samuel Allen Alexander
Keyword(s):  
Mathematical Knowledge ◽  
General Intelligence ◽  
Artificial General Intelligence ◽  
Definition Of

scholarly journals OODA-RL: A REINFORCEMENT LEARNING FRAMEWORK FOR ARTIFICIAL GENERAL INTELLIGENCE TO SOLVE OPEN WORLD NOVELTY

2021 ◽  
Author(s):  
Pamul Yadav ◽  
Taewoo Kim ◽  
Ho Suk ◽  
Junyong Lee ◽  
Hyeonseong Jeong ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Intelligent Agents ◽  
General Intelligence ◽  
Learning Framework ◽  
Artificial General Intelligence ◽  
Open World ◽  
Definition Of

<p>Faster adaptability to open-world novelties by intelligent agents is a necessary factor in achieving the goal of creating Artificial General Intelligence (AGI). Current RL framework does not considers the unseen changes (novelties) in the environment. Therefore, in this paper, we have proposed OODA-RL, a Reinforcement Learning based framework that can be used to develop robust RL algorithms capable of handling both the known environments as well as adaptation to the unseen environments. OODA-RL expands the definition of internal composition of the agent as compared to the abstract definition in the classical RL framework, allowing the RL researchers to incorporate novelty adaptation techniques as an add-on feature to the existing SoTA as well as yet-to-be-developed RL algorithms.</p>

scholarly journals OODA-RL: A Reinforcement Learning Framework For Artificial General Intelligence To Solve Open World Novelty

2021 ◽  
Author(s):  
Pamul Yadav ◽  
Taewoo Kim ◽  
Ho Suk ◽  
Junyong Lee ◽  
Hyeonseong Jeong ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Intelligent Agents ◽  
General Intelligence ◽  
Learning Framework ◽  
Artificial General Intelligence ◽  
Open World ◽  
Definition Of

<p>Faster adaptability to open-world novelties by intelligent agents is a necessary factor in achieving the goal of creating Artificial General Intelligence (AGI). Current RL framework does not considers the unseen changes (novelties) in the environment. Therefore, in this paper, we have proposed OODA-RL, a Reinforcement Learning based framework that can be used to develop robust RL algorithms capable of handling both the known environments as well as adaptation to the unseen environments. OODA-RL expands the definition of internal composition of the agent as compared to the abstract definition in the classical RL framework, allowing the RL researchers to incorporate novelty adaptation techniques as an add-on feature to the existing SoTA as well as yet-to-be-developed RL algorithms.</p>

scholarly journals OODA-RL: A Reinforcement Learning Framework For Artificial General Intelligence To Solve Open World Novelty

2021 ◽  
Author(s):  
Pamul Yadav ◽  
Taewoo Kim ◽  
Ho Suk ◽  
Junyong Lee ◽  
Hyeonseong Jeong ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Intelligent Agents ◽  
General Intelligence ◽  
Learning Framework ◽  
Artificial General Intelligence ◽  
Open World ◽  
Definition Of

<p>Faster adaptability to open-world novelties by intelligent agents is a necessary factor in achieving the goal of creating Artificial General Intelligence (AGI). Current RL framework does not considers the unseen changes (novelties) in the environment. Therefore, in this paper, we have proposed OODA-RL, a Reinforcement Learning based framework that can be used to develop robust RL algorithms capable of handling both the known environments as well as adaptation to the unseen environments. OODA-RL expands the definition of internal composition of the agent as compared to the abstract definition in the classical RL framework, allowing the RL researchers to incorporate novelty adaptation techniques as an add-on feature to the existing SoTA as well as yet-to-be-developed RL algorithms.</p>

Who is in control? Managerial artificial general intelligence (MAGI) for Football

2021 ◽  
pp. 1-6
Author(s):  
Scott McLean ◽  
Gemma J. M. Read ◽  
Jason Thompson ◽  
P. A. Hancock ◽  
Paul M. Salmon
Keyword(s):  
General Intelligence ◽  
Artificial General Intelligence

scholarly journals Individualization of Products and Services with Artificial General Intelligence and General Collective Intelligence

2020 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Online Education ◽  
News Media ◽  
Intelligent Agents ◽  
Collective Intelligence ◽  
Online News ◽  
General Intelligence ◽  
Entertainment Media ◽  
Individual User ◽  
Artificial General Intelligence ◽  
The Individual

INTRODUCTION: With advances in big data techniques having already led to search results and advertising being customized to the individual user, the concept of an online education designed solely for an individual, or the concept of online news or entertainment media, or any other virtual service being designed uniquely for each individual, no longer seems as far fetched. However, designing services that maximize user outcomes as opposed to services that maximize outcomes for the corporation owning them, requires modeling user processes and the outcomes they target.OBJECTIVES: To explore the use of Human-Centric Functional Modeling (HCFM) to define functional state spaces within which human processes are well-defined paths, and within which products and services solve specific navigation problems, so that by considering all of any given individual’s desired paths through a given state space, it is possible to automate the customization of those products and services for that individual or to groups of individuals.METHODS: An analysis is performed to assess how and whether intelligent agents based on some subset of functionality required for Artificial General Intelligence (AGI) might be used to optimize for the individual user. And an analysis is performed to determine whether and if so how General Collective Intelligence (GCI) might be used to optimize across all users.RESULTS: AGI and GCI create the possibility to individualize products and services, even shared services such as the Internet, or news services so that every individual sees a different version.CONCLUSION: The conceptual example of customizing a news media website for two individual users of opposite political persuasions suggests that while the overhead of customizing such services might potentially result in massively increased storage and processing overhead, within a network of cooperating services in which this customization reliably creates value, this is potentially a significant opportunity.

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