MANIC, formerly known as PMML.1, is a cognitive architecture developed by the predictive modeling and machine learning laboratory at University of Arkansas. It differs from other cognitive architectures in that it tries to "minimize novelty". That is, it attempts to organize well-established techniques in computer science, rather than propose any new methods for achieving cognition. While most other cognitive architectures are inspired by some neurological observation, and are subsequently developed in a top-down manner to behave in some manner like a brain, MANIC is inspired only by common practices in computer science, and was developed in a bottom-up manner for the purpose of unifying various methods in machine learning and artificial intelligence. Overview At the highest level, MANIC describes a software agent that, supposedly, will exhibit cognitive intelligence. The agent's artificial brain comprises two major components: a learning system and a decision-making system. Learning system The learning system models the agent's environment as a dynamical system. It consists of an "observation function", which maps from the agent's current beliefs to predicted observations, and a "transition function", which maps from current beliefs to future beliefs in the next time-step. The observation function is implemented with a generative deep learning architecture. It is trained in an unsupervised manner from the observations that the agent makes. The intrinsic representations of those observations become the agents "beliefs". The transition function is trained in a supervised manner, to predict the next beliefs from the current ones. The entire learning system is based loosely on a 2011 paper by Michael S. Gashler that describes a method for training a deep neural network to model a simple dynamical system from visual observations. Decision-making system The decision-making system consists of a planning module and a contentment function. The planning module uses an evolutionary algorithm to evolve a satisficing plan. The contentment function maps from the agent's current beliefs, or anticipated beliefs, to an evaluation of the utility of being in that state. It is trained by reinforcement from a human teacher. In order to facilitate this reinforcement learning, MANIC provides a mechanism for the agent to generate "fantasy videos" that show anticipated observations if a candidate plan were to be executed.

From Wikipedia (CC BY-SA 4.0).

Notable quotes

“Have I need of mad men, that ye have brought this fellow to play the mad man in my presence?”
— Achish king of Gath, Bible, Samuel I, 21:15.
“Get it understood how dangerous these damaged, sick personalities are to ourselves - and above all, to our children, whose traits are taking form and we shall find ways to put an end to them.”
— Donald Ewen Cameron as quoted by Harvey Weinstein in Father, Son and CIA, pg. 101.
“Insanity was not unknown among the wealthy. Indeed, some forms of madness had sometimes been cultivated as fashionable accessories.”
— Matthew Hughes, Majestrum (2006), , p. 60
“Bobby asked him what it was like to be insane. “There is nothing that being insane is like,” Rupert replied.”
— James Morrow, The War of the Worldviews (2002). Originally published in Mars Probes, reprinted in Karen Haber & Robert Silverberg (ed.) Science Fiction: The Best of 2002, p. 272
“Id commune malum; semel insanivimus omnes.”
— It is a common calamity; at some one time we have all been mad.
“Is not this insanity plea becoming rather common? Is it not so common that the reader confidently expects to see it offered in every criminal case that comes before the courts? [...] Really, what we want now, is not laws against crime, but a law against insanity.”
— Mark Twain, "A New Crime" (1870), as reprinted in Essays and Sketches of Mark Twain (1995), ed. Stuart Miller,

Quotes via Wikiquote (CC BY-SA), each with its original source.

MANIC, formerly known as PMML.1, is a cognitive architecture developed by the predictive modeling and machine learning laboratory at University of Arkansas. It differs from other cognitive architectures in that it tries to "minimize novelty". That is, it attempts to organize well-established techniques in computer science, rather than propose any new methods for achieving cognition. While most other cognitive architectures are inspired by some neurological observation, and are subsequently developed in a top-down manner to behave in some manner like a brain, MANIC is inspired only by common practices in computer science, and was developed in a bottom-up manner for the purpose of unifying various methods in machine learning and artificial intelligence.

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Notable quotes

“Have I need of mad men, that ye have brought this fellow to play the mad man in my presence?”
— Achish king of Gath, Bible, Samuel I, 21:15.
“Get it understood how dangerous these damaged, sick personalities are to ourselves - and above all, to our children, whose traits are taking form and we shall find ways to put an end to them.”
— Donald Ewen Cameron as quoted by Harvey Weinstein in Father, Son and CIA, pg. 101.
“Insanity was not unknown among the wealthy. Indeed, some forms of madness had sometimes been cultivated as fashionable accessories.”
— Matthew Hughes, Majestrum (2006), , p. 60
“Bobby asked him what it was like to be insane. “There is nothing that being insane is like,” Rupert replied.”
— James Morrow, The War of the Worldviews (2002). Originally published in Mars Probes, reprinted in Karen Haber & Robert Silverberg (ed.) Science Fiction: The Best of 2002, p. 272
“Id commune malum; semel insanivimus omnes.”
— It is a common calamity; at some one time we have all been mad.
“Is not this insanity plea becoming rather common? Is it not so common that the reader confidently expects to see it offered in every criminal case that comes before the courts? [...] Really, what we want now, is not laws against crime, but a law against insanity.”
— Mark Twain, "A New Crime" (1870), as reprinted in Essays and Sketches of Mark Twain (1995), ed. Stuart Miller,

Quotes via Wikiquote (CC BY-SA), each with its original source.

Overview

MANIC, formerly known as PMML.1, is a cognitive architecture developed by the predictive modeling and machine learning laboratory at University of Arkansas. It differs from other cognitive architectures in that it tries to "minimize novelty". That is, it attempts to organize well-established techniques in computer science, rather than propose any new methods for achieving cognition. While most other cognitive architectures are inspired by some neurological observation, and are subsequently developed in a top-down manner to behave in some manner like a brain, MANIC is inspired only by common practices in computer science, and was developed in a bottom-up manner for the purpose of unifying various methods in machine learning and artificial intelligence. Overview At the highest level, MANIC describes a software agent that, supposedly, will exhibit cognitive intelligence. The agent's artificial brain comprises two major components: a learning system and a decision-making system. Learning system The learning system models the agent's environment as a dynamical system. It consists of an "observation function", which maps from the agent's current beliefs to predicted observations, and a "transition function", which maps from current beliefs to future beliefs in the next time-step. The observation function is implemented with a generative deep learning architecture. It is trained in an unsupervised manner from the observations that the agent makes. The intrinsic representations of those observations become the agents "beliefs". The transition function is trained in a supervised manner, to predict the next beliefs from the current ones. The entire learning system is based loosely on a 2011 paper by Michael S. Gashler that describes a method for training a deep neural network to model a simple dynamical system from visual observations. Decision-making system The decision-making system consists of a planning module and a contentment function. The planning module uses an evolutionary algorithm to evolve a satisficing plan. The contentment function maps from the agent's current beliefs, or anticipated beliefs, to an evaluation of the utility of being in that state. It is trained by reinforcement from a human teacher. In order to facilitate this reinforcement learning, MANIC provides a mechanism for the agent to generate "fantasy videos" that show anticipated observations if a candidate plan were to be executed.

From Wikipedia (CC BY-SA 4.0).

MANIC across IJR

Open questions

are dip manicures bad for your nails?

In the Arcade

Landgrab: Germanicus EditionPaint the map. Hold the line. Take the world. Now starring the Germanicus parody.Landgrab: Germanicus Kent EditionPaint the map. Hold the line. Take the world. Now starring the Germanicus Kent parody.

Studio characters

Germanicus KentThe American city founder with nowhere else to be today.ManicheThe Portuguese footballer with nowhere else to be today.GermanicusOff script, off duty, unreasonably available.

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