Alp agent model that combines reflective and intuitive thinking
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ALP agent model that combines reflective and intuitive thinking. Consciousness as awareness Levels of consciousness can be compiled and sometimes decompiled from one to another Compiling conscious into subconscious thought by reasoning in advance Logic and neural networks

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Alp agent model that combines reflective and intuitive thinking l.jpg
ALP agent model that combinesreflective and intuitive thinking

  • Consciousness as awareness

  • Levels of consciousness can be compiled and

  • sometimes decompiled from one to another

  • Compiling conscious into subconscious thought

  • by reasoning in advance

  • Logic and neural networks

  • The meaning of life


Dual process theory of human thinking l.jpg
Dual Process Theory of Human Thinking

  • Intuitive thinking, which is “tacit”, opaque and automatic, extends perceptual processing to subconscious levels of thought.

  • Reflective thinking, which is self-aware and controlled, can be used to improve conscious thought and communication.

  • Reflective thinking can migrate to the intuitive level, e.g. learning to use a keyboard, play a musical instrument or drive a car.

  • Intuitive knowledge can sometimes be made conscious and explicit, e.g. constructing a formal grammar for a natural language, coaching sports or developing an expert system.


Consciousness in computational logic l.jpg

Consciousness in computational logic

An agent is conscious

when it is awareof what it is doing and why it is doing it.

Computationally, when an agent is conscious,

its behaviour is controlled by a high level program, which manipulates symbols that have meaningful interpretations in the environment.

When the agent is not conscious,

its behaviour is controlled by a lower level program or physical device, whose structure is ultimately determined by the agent’s interactions with the environment.

Logically, when an agent is conscious,

its behaviour is generated proactively by goals and beliefs.

When the agent is not conscious,

its behaviour is determined reactively by (condition-action rules (or input-output associations).

These rules and associations can be represented at different levels in turn,

including both a logical, symbolic level and the lower, physical level of the agent’s body itself.


Consciousness on the london underground l.jpg

Consciousness on the London underground

Goal: If there is an emergency then I get help.

Beliefs:

A person gets help if the person alerts the driver.

A person alerts the driver

if the person presses the alarm signal button.

There is an emergency if there is a fire.

There is an emergency if one person attacks another.

There is an emergency if someone becomes seriously ill.

There is an emergency if there is an accident.

There is a fire if there are flames.

There is a fire if there is smoke.


Compiling by reasoning in advance l.jpg
Compiling by reasoning in advance

  • Use unfolding to reduce the conclusion of the top-level goal:

  • Goal: If there is an emergency then I get help.

  • Beliefs:

    A person gets help if the person alerts the driver.

    A person alerts the driver

    if the person presses the alarm signal button.

  • New goal:

  • If there is an emergency

  • then I press the alarm signal button.

  • Unfoldingreplaces a predicate by its definition, doing

  • backward reasoning in advance of the need to solve goals.


Compiling by reasoning in advance6 l.jpg
Compiling by reasoning in advance

  • Use unfolding to reduce the conditions of the new goal

  • (doing forward reasoning in advance):

  • Goal: If there is an emergency then I press the alarm signal button.

  • Beliefs:

    There is a fire if there are flames.

    There is a fire if there is smoke.

    There is an emergency if there is a fire.

    There is an emergency if one person attacks another.

    There is an emergency if someone becomes seriously ill.

    There is an emergency if there is an accident.

  • New input-output associations (reactive condition-action rules):

    If there are flames then I press the alarm signal button.

    If there is smoke then I press the alarm signal button.

    If one person attacks another then I press the alarm signal button.

    If someone becomes seriously ill then I press the alarm signal button.

    If there is an accident then I press the alarm signal button.


Higher level compared with lower level representation l.jpg
Higher-level compared withlower-level representation

  • The higher-level representation is aware that

  • the goal of pressing the alarm signal button is to get help.

  • The lower-level representation is not aware of the goal.

  • If something goes wrong,

  • for example if the button doesn’t work

  • or the driver doesn’t get help,

  • then the passenger might not realise there is a problem.

  • Also, if the environment changes,

  • and there are better ways of dealing with emergencies,

  • then it would be harder to modify the lower level representation

  • to adapt to the change.


In computing l.jpg
In Computing

  • Lower-level representations are more efficient.

  • Higher-level representations are more flexible,

  • easier to develop, and easier to change.

  • Typically, the higher-level representation is developed first,

  • and then transformed or compiledinto a lower-level representation.

  • Low-level programs can sometimes be decompiledinto equivalent higher-

  • level programs.

  • The higher-level representation can then be modified and recompiled

  • into a new, improved, lower-level form.

  • Legacy systems, developed directly in low-level languages,

  • may not have enough structure to decompile them.

  • But even then it may be possible to approximate them

  • with higher-level programs.


Slide9 l.jpg

Feed-forward neural networks can be decompiled into logic programming form (from Computational Intelligence, Poole, Mackworth, Goebel, 1998)

inputshidden units output

known

new reads

short

home

reads with strength W

if arguably reads with strength W1

and arguably doesn’t read with strength W2

and W = f(2.98 + 6.88W1 – 2.1W2)


Slide10 l.jpg

arguably reads with strength W1 programming form (from Computational Intelligence, Poole, Mackworth, Goebel, 1998)

if known with strength W4

and new with strength W5

and short with strength W6

and home with strength W7

and W1 = f(– 5.25 + 1.98W4 + 1.86W5 + 4.71W6 – .389W7)

arguably doesn’t read with strength W2

if known with strength W4

and new with strength W5

and short with strength W6

and home with strength W7

and W2 = f(.493 - 1.03W4 - 1.06W5 - .749W6 + .126W7)


Slide11 l.jpg

In English programming form (from Computational Intelligence, Poole, Mackworth, Goebel, 1998)

A person will read a paper if there is strong reason to read the paper and there is no sufficiently strong reason not to read the paper. There is a reason to read the paper if the author is known to the person, the topic is new, the paper is short and the person is at home.There is a reason not to read the paper if the author is not known to the person, the topic is old, the paper is long and the person is not at home.


Slide12 l.jpg

The meaning of life (for a wood louse) programming form (from Computational Intelligence, Poole, Mackworth, Goebel, 1998)

A wood louse’s life is without meaning:

If it’s clear ahead, then I move forward.

If there’s an obstacle ahead, then I turn right.

If I am tired, then I stop.


But a wood louse s life may have hidden meaning l.jpg
But a wood louse’s life programming form (from Computational Intelligence, Poole, Mackworth, Goebel, 1998)may have hidden meaning

  • Top-level goals: The louse stays alive for as long as possible and

  • the louse has as many children as possible.

  • Beliefs:

  • The louse stays alive for as long as possible,

  • if whenever it is hungry then it looks for food

  • and when there is food ahead it eats it, and

  • whenever it is tired then it rests, and

  • whenever it is threatened with attack then it defends itself.

  • The louse has as many children as possible,

  • if whenever it desires a mate then it looks for a mate

  • and when there is a mate ahead it tries to make babies.

  • The louse looks for an object,

  • if whenever it is clear ahead then it moves forward, and

  • whenever there is an obstacle ahead and it isn’t the object then it turns right and

  • when the object is ahead then it stops.

  • The louse defends itself if it makes a pre-emptive attack.

  • Food is an object.

  • A mate is an object.


Deriving an input output specification by reasoning in advance l.jpg
Deriving an input-output specification programming form (from Computational Intelligence, Poole, Mackworth, Goebel, 1998)by reasoning in advance

  • Unfolding the louse’s top-level goals generates subgoals:

  • whenever the louse is hungry then it looks for food

  • and when there is food ahead

  • it eats it, and

  • whenever the louse is tired then it rests, and

  • whenever the louse is threatened with attack then it defends itself and

  • whenever the louse desires a mate then it looks for a mate

  • and when there is a mate ahead

  • it tries to make babies.

  • The first subgoal can be written as two subgoals in the simpler form:

  • If the louse is hungry then it looks for food and

  • If the louse is hungry and there is food ahead then it eats it


Sub goals in simplified form l.jpg
Sub-goals in simplified form programming form (from Computational Intelligence, Poole, Mackworth, Goebel, 1998)

  • If the louse is hungry

  • then it looks for food, and

  • If the louse is hungry and there is food ahead

  • then it eats it, and

  • If the louse is tired

  • then it rests, and

  • If the louse is threatened with attack

  • then it defends itself, and

  • If the louse desires a mate

  • then it looks for a mate, and

  • If the louse desires a mate and there is a mate ahead

  • then it tries to make babies.


Sub goals in simplified form16 l.jpg
Sub-goals in simplified form programming form (from Computational Intelligence, Poole, Mackworth, Goebel, 1998)

  • If the louse is hungry

  • then it looks for food, and

  • If the louse is hungry and there is food ahead

  • then it eats it, and

  • If the louse is tired

  • then it rests, and

  • If the louse is threatened with attack

  • then it defends itself, and

  • If the louse desires a mate

  • then it looks for a mate, and

  • If the louse desires a mate and there is a mate ahead

  • then it tries to make babies.


Slide17 l.jpg
An input-output specification in reactive, condition-action rule form (which requires conflict resolution)

  • If the louse is hungry and it is clear ahead

  • then the louse moves forward.

  • If the louse is hungry and there is an obstacle ahead and it isn’t food

  • then the louse turns right.

  • If the louse is hungry and there is food ahead

  • then the louse stops and the louse eats the food.

  • If the louse is tired

  • then the louse rests.

  • If the louse is threatened with attack

  • then the louse makes a pre-emptive attack.

  • If the louse desires a mate and it is clear ahead

  • then the louse moves forward.

  • If the louse desires a mate and there is an obstacle ahead and it isn’t a mate

  • then the louse turns right.

  • If the louse desires a mate and there is a mate ahead

  • then the louse stops and the louse tries to make babies.


A input output specification with conflict resolution compiled into the rules l.jpg
A input-output specification with conflict resolution compiled into the rules

  • If the louse is threatened with attack

  • then the louse makes a pre-emptive attack.

  • If the louse is hungry and the louse is not threatened with attack and it is clear ahead

  • then the louse moves forward.

  • If the louse is hungry and the louse is not threatened with attack and there is an obstacle ahead and it isn’t food then the louse turns right.

  • If the louse is hungry and the louse is not threatened with attack and there is food ahead

  • then the louse stops and the louse eats the food.

  • If the louse is tired and the louse is not threatened with attack and the louse is not hungry

  • then the louse rests.

  • If the louse desires a mate and it is clear ahead and the louse is not threatened with attack and the louse is not hungry and the louse is not tired

  • then the louse moves forward.

  • If the louse desires a mate and there is an obstacle ahead and it isn’t a mate and the louse is not threatened with attack and the louse is not hungry and the louse is not tired

  • then the louse turns right.

  • If the louse desires a mate and there is a mate ahead and the louse is not threatened with attack and the louse is not hungry and the louse is not tired

  • then the louse stops and the louse tries to make babies.


Slide19 l.jpg

ALP agent that combines compiled into the rulesconscious and subconscous thinking and that is aware of the meaning of its life

Achievement goal

Maintenance goal

Judge

probabilities

and utilities

Forward

reasoning

Backward

Reasoning

Consequences

Consequences

Consequences

Forward

reasoning

Decide

Observe

Input-output associations

Act

The World


Epilog l.jpg
Epilog compiled into the rules

  • Modeling Physical Skill Discovery and Diagnosis by AbductionIkuo Kobayashi1) and Koichi Furukawa2)1) SFC Research Institute, Keio University2) Graduate School of Media and Governance, Keio University(Received: August 25, 2007)Abstract:We investigate an Abductive Logic Programming (ALP) framework to find appropriate hypotheses to explain both professional and amateur skill performance, and to distinguish and diagnose amateur faulty performance. In our approach, we provide two kinds of rules: motion integrity constraints and performance rules. Motion integrity constraints are essential to formulate skillful performance, as they prevent the generation of hypotheses that contradict the constraints. Performance rules formulate the problem of achieving difficult physical tasks in terms of preferred body movements as well as preferred muscles usage and preferred posture. We also formulate the development of skills in terms of default logic by considering the basic skills as defaults, and advanced skills as exceptions. In this case, we introduce preferences in integrity constraints: either hard integrity constraints to be always satisfied or soft integrity constraints which can be ignored if necessary. Finally we apply this framework to realize skill diagnosis.


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