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Numéro de publicationUS20020138246 A1
Type de publicationDemande
Numéro de demandeUS 09/802,505
Date de publication26 sept. 2002
Date de dépôt8 mars 2001
Date de priorité8 mars 2001
Autre référence de publicationUS7499893, US7849026, US8065245, US8346699, US20060142986, US20090198480, US20110082822, US20120066164, WO2002073428A1, WO2002073428A8
Numéro de publication09802505, 802505, US 2002/0138246 A1, US 2002/138246 A1, US 20020138246 A1, US 20020138246A1, US 2002138246 A1, US 2002138246A1, US-A1-20020138246, US-A1-2002138246, US2002/0138246A1, US2002/138246A1, US20020138246 A1, US20020138246A1, US2002138246 A1, US2002138246A1
InventeursGregory Czora
Cessionnaire d'origineCzora Gregory J.
Exporter la citationBiBTeX, EndNote, RefMan
Liens externes: USPTO, Cession USPTO, Espacenet
System and method for simulating conciousness
US 20020138246 A1
A simulated consciousness method (10) for an improved human/computer interface. A computer system (12) is programmed to have a Digital Life Form (32) possessing a plurality of attributes (65). A plurality of actions (64) taken relative to objects (60) in the environment (30) contribute to simulated feelings (76) which ultimately control the viability of the Digital Life Form (32). When there are not sufficient energy packets (66) to sustain the Digital Life Form (32) then simulated death 52 results. Therefore, only actions (64) which contribute to the viability of the Digital Life Form (32) are repeated in the long-run. Some of those actions (64) include perception of reality, concept formation, and natural language processing.
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I claim:
1. A computer generated entity, comprising:
a plurality of attributes, wherein at least one such attribute defines the vitality of the entity; and
a plurality of actions, at least one of which will affect the vitality of the entity.
2. The computer generated entity of claim 1, wherein:
said actions simulate actions by the entity on objects in an environment.
3. The computer generated entity of claim 2, wherein:
the environment is a computer generated simulated environment.
4. The computer generated entity of claim 1, wherein:
simulated death occurs when the actions result in a reduction of vitality below a preset level.
5. The computer generated entity of claim 1, wherein:
vitality level is determined by a quantity of energy packets.
6. A computer interface, comprising:
a digital form having a plurality of attributes;
a plurality of actions which may be accomplished by the digital life form; and
a selection criteria for selecting from said plurality of actions; wherein repeated selection of actions which do not contribute to the vitality of the digital life form will result in the simulated death of the digital life form
7. The computer interface of claim 6, and further including:
a birth process wherein experience from the digital life form is passed on to a next generation of the digital life form.
8. The computer interface of claim 6, wherein:
said digital life form perceives a plurality of objects in an environment; and
said actions are selected to optimize vitality dependant upon the particular objects perceived.
9. The computer interface of claim 6, wherein:
said actions are taken to optimize at least one of a plurality of simulated feelings.
10. The computer interface of claim 9, wherein:
at least one of the simulated feelings is a feeling of fullness.
11. The computer interface of claim 10, wherein:
the feeling of fullness is represented by a quantity of energy packets.
12. A computer program product comprising a computer usable medium having a computer readable program code embodied thereon configured to operate on a computer, comprising:
code to cause the computer to keep track of a list of attributes of a digital life form;
code to cause the computer to cause the digital life form to take actions to maintain its own vitality.
13. The computer program product of claim 12, wherein:
said actions are selected form a list of actions programmed into the computer.
14. The computer program product of claim 12, wherein:
at least one consequence of the selection of said actions is the termination of the digital life form.
15. The computer program product of claim 12, wherein:
at least one of the attributes of the digital life form is a simulated feeling.
16. A method for creating a digital life form, comprising:
defining a digital life form;
providing access for the digital life form to an environment;
defining a plurality of potential actions for the digital life form; and
providing consequences to the digital life form for such actions.
17. The method of claim 16, wherein:
said digital life form includes a plurality of attributes.
18. The method of claim 16, wherein:
said environment is a computer generated simulated environment.
19. The method of claim 16, wherein:
at least one of said actions includes EAT.
20. The method of claim 19, wherein:
EAT is defined as assimilating energy packets to increase the vitality of said digital life form.
21. The method of claim 16, wherein:
at least one consequences of said actions is the simulated death of said digital life form.
22. The method of claim 16, and further including:
providing a strategy for selecting from said plurality of actions.
23. A method for simulating consciousness, comprising;
identifying characteristics of objects in an environment; and
storing lists of said characteristics.
24. The method of claim 23, and further including:
identifying at least ?? of said objects by comparison to said lists of said characteristics.
25. The method of claim 23, and further including:
acting on at least one of said objects according to the characteristics of that object.
26. The method of claim 23, wherein:
said lists are percepts.
27. A method for obtaining information valuable for survival, comprising:
performing a plurality of optional behaviors; and
storing information relative to such actions for future reference.
28. The method of claim 27, wherein:
said actions are initially selected in a random manner.
29. A method for forming concepts in a Digital Life Form, wherein:
percepts are compared to form concepts.
30. The method of claim 29, wherein:
concepts are compared to form conceptual chains.
31. The method of claim 29, wherein:
concepts are associated with natural language words.
  • [0001]
    A portion of the disclosure of the patent document contains material which is subject to copyright protection. The owner has no objection to the facsimile reproduction by any one of the patent disclosure, as it appears in the Patent and Trademark Office patent files of records of any country, but otherwise reserves all rights whatsoever.
  • [0002]
    The present invention relates to the field of software for computers and related devices, and more particularly to a method for causing a computer or other such device to interact with human beings as though the device has human like consciousness. The predominant current usage of the present inventive method for simulating consciousness is in the improvement of communication in human/machine interaction.
  • [0003]
    It is known in the art to cause a computer to emulate certain functions that are traditionally associated with human behavior. For example, efforts at artificial intelligence (“AI”) generally attempt to provide knowledge in response to inquiries. However, known AI systems merely respond with information that has been programmed into them. That is, a machine programmed with an AI program merely responds in the manner in which its human programmers provided for when the program was written.
  • [0004]
    Experiments in the field of artificial life (“AL”) attempt to cause a machine to function or respond to external stimuli in a manner generally associated with a living organism. While such experiments are providing a foundation for future useful devices, the machine/human interface is not much enhanced by the present state of the art in this field.
  • [0005]
    It is recognized in the field that it would be valuable to have a computer which does not respond in some preprogrammed automatic manner. Genetic algorithms have been devised which attempt to get around this problem by emulating or recapitulating evolution, in the hope that eventually intelligence will emerge. Neural networks have attempted to do something similar by emulating the function of neurons in higher life forms. While it is possible that these methods might eventually help to solve some aspect of the problem, there has not yet been any useful benefit derived from such experiments.
  • [0006]
    It would be beneficial to have a machine/human interface which approaches the flexibility of a human/human interface. However, all known efforts in the field have been limited to either providing a particular preprogrammed response to an inquiry, or else have not provided a useful interface between a user and the machine.
  • [0007]
    Accordingly, it is an object of the present invention to provide a machine/human interface which reacts to stimuli in a manner generally associated with an animal or a human being.
  • [0008]
    It is still another object of the present invention to provide a machine which simulates consciousness.
  • [0009]
    It is yet another object of the present invention to provide a computer program which will cause a computer to develop a simulated consciousness.
  • [0010]
    It is still another object of the present invention to provide a method and apparatus for interfacing with a human being as though said apparatus possesses consciousness.
  • [0011]
    It is yet another object of the present invention to provide a method and apparatus for causing a machine to appear to possess consciousness.
  • [0012]
    It is still another object of the present invention to provide a method and apparatus for improving a computer/user interface.
  • [0013]
    It is yet another object of the present invention to provide an improved computer/user interface.
  • [0014]
    Briefly, a known embodiment of the present invention is a computer program which establishes goal directed behavior. A computer is programmed to define actions which can either increase or decrease simulated happiness scores and which can result either in the continued existence of a simulated life form or else the demise thereof. Only actions which tend to perpetuate the simulated life will be repeated in the long run. In this manner, a Digital Life Form will be goal directed and will, therefore, act in a manner much as though it is alive and has actual consciousness. The Digital Life Form can exist entirely within a computer program for simulation purposes, or can be tied to the “real world” using sensors, and the like, for practical applications.
  • [0015]
    The Digital Life Form, thereby, acts as a teleological agent. An advantage of the complexity of teleological agents is that they can find ways to do tasks for which they were not programmed.
  • [0016]
    According to the present invention, simulated consciousness is a series of discrete causal steps performed by program methods that repeat or cycle operations which a programmer turns into a process by putting them into a loop internal to the Digital Life Form, in order to simulate its life and consciousness. The program continuously cycles through these several program methods, thus effecting the simulation. The process steps to simulate consciousness run in a subsystem layer above those of the Digital Life Form's simulated life processes, and the program methods that implement them are to cause the Digital Life Form to perceive its environment, evaluate objects therein, select an action, act, and record the action and results thereof to memory. Such action is repeated ad infinitum so long as the Digital Life Form remains “alive”.
  • [0017]
    An advantage of the present invention is that a machine can interface with a human being in a manner generally associated with a human to human interaction.
  • [0018]
    A further advantage of the present invention is that it is easier for a human to interface with and use a computer.
  • [0019]
    Yet another advantage of the present invention is that a computer can be caused to develop a simulated consciousness with only a minimal amount of programming.
  • [0020]
    Still another advantage of the present invention is that it will be easier and more natural to use a computer or computerized machine.
  • [0021]
    Yet another advantage of the present invention is that it will be readily implemented using available computer hardware and input/output devices.
  • [0022]
    These and other objects and advantages of the present invention will become clear to those skilled in the art in view of the description of modes of carrying out the invention, and the industrial applicability thereof, as described herein and as illustrated in the several figures of the drawing. The objects and advantages listed are not an exhaustive list of all possible advantages of the invention. Moreover, it will be possible to practice the invention even where one or more of the intended objects and/or advantages might be absent or not required in the application.
  • [0023]
    Further, those skilled in the art will recognize that various embodiments of the present invention may achieve one or more, but not necessarily all, of the above described objects and advantages. Accordingly, the listed advantages are not essential elements of the present invention, and should not be construed as limitations.
  • [0024]
    [0024]FIG. 1 is a flow diagram depicting an embodiment of a simulated awareness method, according to the present invention.
  • [0025]
    [0025]FIG. 2 is a diagrammatic view of a general purpose computer system such as may be used for practicing the present inventive method;
  • [0026]
    [0026]FIG. 3 is a simulated environment, including a digital life form, according to the presently described embodiment of the invention;
  • [0027]
    [0027]FIG. 4 is a flow diagram depicted a somewhat more complicated simulated consciousness method;
  • [0028]
    [0028]FIG. 5 is a flow chart depicting a simulated feeling, as shown in FIG. 4;
  • [0029]
    [0029]FIG. 6 is a flow diagram depicting an example of a method for creating a simulated consciousness;
  • [0030]
    [0030]FIG. 7 is a diagrammatic representation of a hierarchical process according to the present invention; and
  • [0031]
    [0031]FIG. 8 is a diagrammatic representation of a concept chain such as might be formed according to the present invention.
  • [0032]
    While this invention is described in terms of modes for achieving this invention's objectives, it will be appreciated by those skilled in the art that variations may be accomplished in view of these teachings without deviating from the spirit or scope of the present invention. For example, the present invention may be implemented using any combination of computer programming software, firmware or hardware. As a preparatory step to practicing the invention or constructing an apparatus according to the invention, the computer programming code (whether software or firmware) according to the invention will typically be stored in one or more machine readable storage devices such as fixed (hard) drives, diskettes, optical disks, magnetic tape, semiconductor memories such as ROMs, PROMs, etc., thereby making an article of manufacture in accordance with the invention. The article of manufacture containing the computer programming code is used by either executing the code directly from the storage device, by copying the code from the storage device into another storage device such as a hard disk, RAM, etc. or by transmitting the code on a network for remote execution. The method form of the invention may be practiced by combining one or more machine readable storage devices containing the code according to the present invention with appropriate standard computer hardware to execute the code contained therein. An apparatus for practicing the invention could be one or more computers and storage systems containing or having network access to computer program(s) coded in accordance with the invention.
  • [0033]
    A presently known mode for carrying out the invention is a computer program, operative on a general purpose computer, for accomplishing the inventive method as described herein. An example of an inventive simulated awareness method is depicted in a flow diagram in FIG. 1 and is designated therein by the general reference character 10. FIG. 2 is a block diagram of a computer system 12 such as is anticipated to be used to accomplish the simulated consciousness method 10. Illustrated is a general purpose computer 14, having the usual appendages such as a keyboard 16, a pointing device 18 (generally a mouse), a display screen 20, and a printer 21, a removable medium 22 (such as a floppy disk or CD ROM) in a removable medium drive 24, and a fixed medium drive 26. The inventive simulated awareness method 10 will generally be stored upon the removable medium 22 for downloading into the fixed medium drive 26 of the computer system 12. In addition, a data base 28 consisting of a data to be used with the present inventive method will generally be stored on the fixed medium 26.
  • [0034]
    According to the present inventive method, goal directed behavior is used to simulate the sort of response usually associated with a conscious being. A primary goal is the “survival” of a digital life form (“DLF”). A diagrammatic representation of a simulated environment 30 including a DLF 32 is depicted in the view of FIG. 3 and will be discussed in greater detail, hereinafter.
  • [0035]
    In the presently described example of the invention, the “life” of the DLF 32 is represented numerically in the computer system 12. This simple concept will be familiar to those practiced in the art of computer games, wherein a numerical score is used to represent the relative vitality of a character. However, an essential difference here is that the vitality of the DLF is maintained by the actions of the DLF itself, and as such it is a conditional entity.
  • [0036]
    Referring again to FIG. 1, the simulated awareness method 10 functions as an endless loop (with exceptions as discussed hereinafter) wherein an action 40 attempts to achieve a goal 42 which, if successful (as determined in a success decision operation 44 will result in the survival 46 of the DLF 32 (FIG. 3). Subsequently, another action 40 is selected in a select action operation 48, and an experience tally 50 is incremented. These operations will be discussed in more detail hereinafter. As can be seen in the view of FIG. 1, should the action 40 not be successful (or alternatively, should successive actions not be successful, as will be discussed hereinafter), then the DLF 32 is deactivated, simulating the “death” 52 of the DLF 32, as consistent with its conditional nature.
  • [0037]
    The present inventive DLF 32, as with any life form, must cause its own future existence (survival) precisely because it is a goal directed or internally driven entity, as opposed to a rock, which is not goal directed. Any actions of a rock are simply the result of outside forces. Failure to maintain goals causes the life form to cease to exist, a condition in which it is no longer part of reality and one that is irreversible. Only behaviors that are successful will be repeated in the long run, as will be discussed in more detail, hereinafter.
  • [0038]
    Prior to the present invention, there has been a profound and fundamental difference between state of the art computer systems and biological life forms, between mechanical/logical systems and teleological systems. If something is a real life form, that is, if it is alive, it must be conditional, because that is the essential attribute of all life forms. The artificial life form (DLF 32) should therefore be goal directed, which means that it be internally driven by its own values, energy source, internal locus of control, and the value significance to itself of its own values. In order to act according to the present inventive method, the DLF 32 should have values (or an equivalent thereof) and act to gain and keep them on its own power. Simulated death is the primary means this invention uses to solve the problem of the apparent need to predefine a simulated life form's future actions. Simulated death solves this problem because only pro-life actions get repeated.
  • [0039]
    Referring again to FIG. 3, it can be seen that in the simulated environment 30 the DLF 32 exists along with a plurality of external objects 60. These objects could represent things such as food 62, which would contribute to the viability of the DLF 32. Another example would be that an object 63 could represent a threat to the DLF 32 if the DLF 32 does not take action to avoid it.
  • [0040]
    According to the present inventive method, a DLF 32, just like a living organism, must take in materials and energy from the environment 30, and it must use the appropriate materials and energy for self maintenance, self repair and self reproduction. And, also like a living organism, once the DLF 32 has died, it cannot be reconstituted-failure is irreversible. In order for a DLF to appear to have consciousness, its primary purpose cannot be to achieve human goals, which is how conditional programming structures are used in all state of the art computer programs, but the goals of the DLFs 32 themselves.
  • [0041]
    This means that DLFs 32 must be logically structured to take action to maintain their existence, and that they must be deleted if their survival actions fail.
  • [0042]
    Accordingly, the DLFs 32 must be equipped with a pallet of potential actions 64 through which it can interact with the objects 60 in its environment 30. Human programmers can predefine basic actions such as look, find, or eat, to build a starter simulation system goal directed action refers to actions (or sequences of basic actions) selected by a life form for survival purposes.
  • [0043]
    Still referring to the view of FIG. 3, it can be seen that the DLF 32 has several attributes 65, examples of which are shown in FIG. 3. In a slightly more sophisticated example, the DLF 32 might possess simulated “feelings”. An example of attributes 65 for a DLF possessing simulated feelings might be as follows:
  • [0044]
    1.Name: 006023
  • [0045]
    2.Age: 84
  • [0046]
    3.Starting Energy Packets (“EPs”): 100
  • [0047]
    4.Current EPs: 350
  • [0048]
    5.Current percepts: P1, P2, . . . Pn
  • [0049]
    6.Actions Available: Look, Find Food, Eat, Stop
  • [0050]
    7.Simulated Feelings:
  • [0051]
    a. Hunger/Fullness: −2
  • [0052]
    b. Interest/Boredom: +3
  • [0053]
    c. Company/Loneliness: +2
  • [0054]
    d. Clarity/Confusion: +5
  • [0055]
    e. Activity/Laziness: −1
  • [0056]
    f. Confidence/Fear: +2
  • [0057]
    g. Happiness: 1.5
  • [0058]
    A programmer skilled in object oriented programming can make simulated feelings attributes of a class of DLF 32 program objects. The simulated feelings give the DLF 32 an instantaneous indication of its life status, and, if put into a window on the computer screen as part of a DLF 32 program interface, a human observer can see the same status. By being conscious of its own life status, a DLF 32 can take actions to cause its future survival, since it would have the information that is a prerequisite to such actions. Simulated feelings are the simplest form of simulated self awareness or self consciousness, though at this level a DLF 32 is not aware that it is aware of itself.
  • [0059]
    As can be seen from the example above and that of FIG. 3, a DLF 32 can have attributes 65 such as a quantity of energy packets (“EPs”) 66 which represent its degree of vitality. When a DLF 32 reaches zero EPs 66, its life would end. Therefore, maintaining an adequate energy supply (sufficient EPs 66) becomes the basis for all other actions a DLF 32 may be capable of performing. Therefore, once the DLF 32 programming object has been created and defined, processes called methods (object oriented computer programming code) must be defined to enable the DLF 32 to take action and an action selection method to enable internal control of its actions to find simulated food in its simulated environment to generate more EPs 66. This must be a continuous process to enable the DLF 32 to survive, just like a biological life form. These methods define the actions 64 depicted in the view of FIG. 3.
  • [0060]
    As seen in the view of FIG. 4, the DLF 32 can include one or more percepts 67. As defined herein, a percept 67 is a list of the perceived characteristics of the objects 60 that is calculated from input sensed by the DLF 32 from the objects 60 in its environment 30. Each percept 67 is a list of the properties and values (property measurements) of a corresponding object 60. To the DLF 32, the percepts 67 are the identities of the objects 60. Therefore, the percepts 67 are the processing units of simulated perceptual consciousness in a DLF 32, as will be discussed in more detail hereinafter.
  • [0061]
    As with the DLF 32 program object itself, the program objects 60 in the DLF's 32 simulated environment 30 must be created and defined (to save resources and make the system simpler during initial development), but since these objects 60 are non-conditional (non-living), most need few action methods for simple reality simulations. More complex and sophisticated simulated environments (not shown) in which nonliving objects are animated (or contain other DLFs 32), would however, require coding extensive action methods for those objects.
  • [0062]
    By way of example, the program code for an “Eat” method 69 can automatically include digestion, generating energy EPs 66, and the simulated feeling of being “full”. The code for a “Stop” method might be is a simple loop that continuously tests for feeling of fullness, and stops the Eat method when that condition is met. The code for the Death 52 method erases the current DLF 32 from the computer's memory and calls the Birth method which increments the DLF 32 name attribute by one and resets the other attributes to initial conditions.
  • [0063]
    It will be advantageous to save pro-life behaviors and maintain them between generations of the DLFs. This may be done by not erasing the behaviors from memory at simulated death 52, thereby simulating genetic evolution to carry the behaviors forward to the next generation of DLFs 32. Alternatively, some other method not yet contemplated by the inventor might be used for this purpose. In any event, it is important that the only actions that get repeated long term are the valuable actions. Life forms (DLFs 32) that repeat any other kind of actions simply get wiped out and no longer exist, and only actions of those DLFs 32 that are relatively successful should be carried forward to subsequent generations.
  • [0064]
    The complexity of the program code for sensing the environment 30 will differ greatly depending on whether the environment for a DLF 32 is simulated or real. The two types of environment are essentially equivalent, except that real sensors sensing reality provide much more accurate and detailed real time data of the world, whereas simulated worlds are limited to human imagination and computing resources. Simulated environments are primarily useful for developing, testing, and proving program methods while conserving resources. Sophisticated simulations intended for practical uses will need to interact with the real world to be effective.
  • [0065]
    An example of a slightly more complex simulated consciousness method 10 a is depicted in the view of FIG. 4. In a perceive environment operation 70 objects 62 in the environment 30 are located and then identified. In this simple example, the only objects 60 of interest are food 62. If food is not found, a check is made to determine if there are sufficient EPs 66 to maintain existence. If not, the death 52 operation is called, wherein the DLF 32 is deleted from memory and a birth operation 72 is called to create a new DLF 32. If there are EPs 66 to continue, the loop returns to the perceive environment operation 70. When food 62 is identified, the program proceeds to an eat operation 74 wherein the food 62 is assimilated and used to create EPs 66. This process is continued until there is no more food 62 immediately available or else until the DLF 32 is “full”—that is, until it has achieved its maximum quantity of EPs 66.
  • [0066]
    As can be appreciated in light of the above discussion and the flow diagram of FIG. 4, once various objects 60 have been perceived by a DLF 32, they must be evaluated with the DLF's 32 life as the standard of value. To a biological life form, since its continued existence is conditional, every percept is either a value or a disvalue relative to its life. That is, every percept has value significance to the life form as being information about its world that is either for or against its life. In order for a DLF 32 to be an accurate simulation of a life form, therefore, a DLF 32 should also be able to determine the value significance of its percepts 67 (FIG. 3). One way to accomplish this is to simulate pleasure and/or pain, in much the same way the other generally biological functions have been simulated as discussed previously herein. For example, in FIG. 4, a “feeling” operation 76 calculates whether or not the DLF 32 is experiencing the feeling of being “full”. In like manner other feelings can be simulated.
  • [0067]
    The pleasure/pain systems of biological life forms are automatic, built in value systems. In general, things that are good for a life form cause it to feel pleasure, and things that are bad for it cause it pain (either physical, emotional, or both). In order to create a digital simulation of a life form a similar automatic, built in evaluation system is desirable and, like actions, this can be copied from biological life forms and predefined so evolution does not have to be recapitulated by DLFs 32. Since computers are not biological, simulated pleasure and pain must be calculated based on simulated values which serve as standards with the life of a DLF 32 being the ultimate standard. The ideal is to make simulated evaluations as causally and functionally equivalent to the biological ones as is technically possible. An example of a flow chart for calculating a simulated feeling is depicted in the view of FIG. 5. For example, to calculate if the DLF 32 feels “full” in computational terms, a method must is written that compares the number of EPs 66 that a DLF 32 has with the range that its simulated life requires. Having EPs 66 is a value to a DLF's 32 life; without them the DLF 32 will die just as a biological life form will die without food. A simulated feeling 76 can be calculated for any number of EPs 66 a DLF 32 has at any specific time by comparing the number it actually has to its required range. As can be seen in the view of FIG. 5, in this example, the feeling 76 is calculated by a getting current EPs operation 80, then a comparing value operation 83 wherein the current EPs 66 are compared to a set range of acceptable values, then the feeling 76 is calculated in a calculate feeling operation 84, based upon where the current EP 66 quantity lies within this spectrum. Finally, the calculated feeling 76 is stored as an attribute of the DLF 32 in a store attribute operation 86. The attributes of the DLF 32 are discussed above and in relation to FIG. 3.
  • [0068]
    Early in a DLF's 32 life, when there are few examples of percepts 67 and how the DLF's 32 previous actions changed them, most of the DLF's 32 actions will be selected by trial and error. However after an extended life and, perhaps, many thousands of perception/action events, the action selection methods will have much more data to use and will, therefore, be able to select actions with the greatest survival value more efficiently. Some examples of action strategies that might be provided by a programmer are as follows:
  • [0069]
    Continue the last action: This is a useful strategy when an action is succeeding in improving simulated feelings (such as eating to reduce hunger).
  • [0070]
    Select the action that resulted in pleasure in the past when a given object was perceived: This option is similar to the previous one, but is recalled from a memory association from the past.
  • [0071]
    Select no action: This is a useful option when all simulated feelings are positive and no action is required to change them. It is also an example of an optional action. Follow a pre-programmed process (when a given object is perceived, as with instinctual behavior in biological life forms such as nest building, or habits in humans): This option is a good strategy for a goal requiring complex actions or series of actions.
  • [0072]
    Random action selection: This option is analogous to trial and error actions observed in biological life forms and is useful for new situations when no other action gets selected. It is another example of an optional action.
  • [0073]
    According to the present invention, actions are not preselected, but rather are selected by simulating the perceptual consciousness process and, as with its biological counterpart, this process is an automatic one (in the teleological sense). There is no other basis for making selections because options are limited at the perceptual level. However, action selection is teleological because its goal is a DLF's 32 survival, the DLF's 32 simulated life is the standard and it, therefore, cannot be explained as simple mechanistic, billiard ball type of causality.
  • [0074]
    When creating action selection methods the following points should be considered. An action selection method should insure that some action is always selected for any perceptual event. An action selection method should be teleological in that its goal is causing the survival of the DLF 32 with its simulated life as the standard value, and does so by increasing the DLF's 32 simulated happiness. Only survival actions get repeated in the long run. “No_Act” and/or “Random_Act” methods can allow for a DLF 32 to maintain its simulated happiness for a time, provide for trial and error actions, and allow for the unexpected or the novel event to be simulated.
  • [0075]
    It will be recognized by one skilled in the art that after a great many “experiences” by the DLF there will accumulate a great deal of data. Therefore, it may be desirable to divide the data base 28 (FIG. 2) to have both short and long term storage wherein much of duplicate short term information is deleted FIG. 6 is a flow diagram depicting an example of a process 90 for creating a simulated consciousness method 10. In the example of FIG. 6, it can be seen that in a define DLF operation 92 the attributes 65 for a DLF 32 are determined and defined, and provision is made to store such in the data base 28 of the computer 14. Again, one skilled in the art of object oriented programming will appreciate that this is a relatively simple process. In a provide access to environment operation 94 provision is made for allowing the DLF 32 to perceive its environment 30. As discussed previously, herein, the nature of this operation will vary according to the nature and complexity of the environment 30. If the environment 30 is entirely simulated, as in the simple example of FIG. 3, then the programmer can merely define the objects 60 in the environment 30 as program objects. Alternatively, if the DLF 32 were to be intended to operate in the “real world” then sensors could be provided to sense real world objects (not shown) and identify them. The technology for this currently exists and is being further developed, and is not an aspect of this particular invention.
  • [0076]
    In a provide selection of actions operation 96, a programmer will define selected actions 64, as previously discussed herein, and will further define the circumstances under which particular actions 64 will be selected. In a define consequences operation 98, the programmer will provide for the simulated feelings 76 which will assist in determining the appropriate action 64. Also, as previously discussed herein, the consequence of simulated death 52 and birth will be programmed.
  • [0077]
    [0077]FIG. 7 is a diagrammatic representation of a hierarchical process 100 such as will enable a DLF 32 to achieve simulated consciousness. As can be seen in the view of FIG. 1, the DLF 32 will first form percepts 67 in a percept formation operation 102 such as has been discussed previously herein. It should be noted that many percepts 67 will be created, essentially one for each object 60 or entity encountered in the environment 30 of the DLF 32. Therefore, the diagram of FIG. 7 is not a flow diagram, but rather a hierarchical diagram showing the levels of operation of the DLF. As can be appreciated by one skilled in the art the percept formation operation 102 will be repeated, as necessary, as objects 60 are encountered in the environment 30.
  • [0078]
    [0078]FIG. 8 is an example of a concept chain 104, which will be discussed hereinafter in relation to the remainder of FIG. 7. When the DLF 32 has stored sufficient percepts 67 to make comparisons, a concept 106 can be formed by such comparison. For example, any shapes which are closed, and comprised of three straight sides and three corners can be grouped together to form a concept 106 “triangle”. When sufficient concepts 106 have been formed for comparison, these can be compared to make additional concepts 106. In the example of FIG. 8 it can be seen that the concepts 106 “triangle”, “circle” and “square” have similar characteristics which can be grouped under the concept 106 “closed shape”. In like manner, the entire concept chain 104 of FIG. 8 can be formed, given sufficient experience by the DLF 32. Higher level concepts 106 are formed by comparing the attributes of lower level concepts 106, as can be seen in the view of FIG. 8.
  • [0079]
    It should be noted that concepts 106 can be formed by comparison of certain particular attributes of percepts 67. For example, looking only at the relative position of objects 60 can lead to the formation of concepts such as “above”, “to the right of” and the like. Likewise, concepts can be formed relating to intangibles. That is, concepts are calculated for objects, actions, relationships and even for other concepts.
  • [0080]
    Referring again to FIG. 7, it can now be appreciated that a next level of operation of the DLF 32 following percept formation 102 will be concept formation 108, wherein concepts 106 are formed, as discussed above. One skilled in the art will recognize that concept formation 108 will not be an inherent characteristic of a DLF 32, but rather will be provided for as one of the actions 40 available to the DLF (much like “eat”, or the like), which have been discussed previously herein and may require multiple simulated conscious loop cycles to complete.
  • [0081]
    It should be noted that the formation of concepts 106 does not inherently provide for a name for the concepts such as have been used to discuss the example of FIG. 8. That is, just because the DLF 32 recognizes the similarities between objects such that it can group all triangular shaped objects together by such similar characteristics, that does not mean that the DLF will understand that these are called “triangles”. This may be referred to as an “implicit concept”, wherein the DLF 32 has the data to form a concept, but does not yet have a name for it. As discussed above, concept formation is a form of simulated volitional (free-will) behavior. Percepts 67 are calculated automatically. Concepts 106 are also calculated as optional behavior, this being non-automatic action. But what the computer 12 cannot do on its own is to come up with a real language word. The computer 12 could come up with its own word, but then it would have to be translated in order for the computer 12 to communicate with the real world. In order to provide real English word, a human tutor should interact with the DLF much like a child would learn. The ability to decode and encode sentences will depend on concepts, also. The DLF 32 will perceive words as objects 60 and can form concepts 106 of sentences and the parts of sentences. This process is represented diagrammatically in a word association operation 110 in FIG. 7, wherein a concept 106 is associated with a word, as discussed above. Once concepts 106 are formed, as shown in FIG. 7, the encoding of a sentence may follow. This is a process that starts with objects 60 and connects them to the words that make up the sentence. Reversing the arrows would be the decoding of a sentence, essentially by reconnecting the words in the sentence to objects 60 in reality. Both processes operate by tracing previously calculated conceptual chains 112, or in some cases, by calculating new ones. In the view of FIG. 8 it can be seen that each of the concepts 106 is represented by a natural language word 112 in the concept chain 112. Simulated perception, concept formation, and the processes of encoding and decoding sentences, taken together as described herein, solve the problem known in the state of the art as natural language understanding and production.
  • [0082]
    Various modifications may be made to the invention without altering its value or scope. For example, alternative or additional actions, methods, and the like might be used instead of or combined with those described herein. One additional action method could be the ability to compare the characteristics of the objects a DLF perceives, group and abstract percepts by similarity. Another example of an obvious modification would be to incorporate the invention into a robotic device or other such machine, instead of the general purpose computer used in the example of this disclosure.
  • [0083]
    All of the above are only some of the examples of available embodiments of the present invention. Those skilled in the art will readily observe that numerous other modifications and alterations may be made without departing from the spirit and scope of the invention. Accordingly, the disclosure herein is not intended as limiting and the appended claims are to be interpreted as encompassing the entire scope of the invention.
  • [0084]
    The inventive simulated awareness methods 10 and 10 a are intended to be used in ever increasingly complex forms to eventually result in a DLF 32 which can interact with humans and the “real” world in which we live, thereby resulting in a program which appears to have consciousness and which can solve problems for which it is not specifically programmed.
  • [0085]
    A relatively short development time is provided for, since this invention copies many design ideas from real life forms, instead of attempting to re-evolve them to recapitulate evolution in some manner, such as is attempted by genetic algorithms, and the like. In other words, just as the AL researchers did not re-evolve the gait of insect robots, but rather reverse engineered their operation by copying real life forms, so this invention seeks to reverse engineer the simulation of goal directed behavior and consciousness rather than re-evolve it. A key is to identify the essential elements and program substitutions. This is the pre-defined part of the simulation system. This aspect of the design involves identifying the necessary and sufficient set of elements to develop the substitutions for, and then writing the software code for those elements. This self defining stage of the development of the simulation system is the management and tutoring of those basic elements as they simulate the active processes of life and consciousness.
  • [0086]
    The inventive method can be practiced using a reasonably powerful desktop computer with at least 64MB of memory, a 1 GB hard disk drive, and an object oriented programming environment to write a goal directed program that simulates a life form. Writing a program to simulate goal directed behavior on the computer system 12 amounts to creating the DLF 32 and the simulated environment 30 in which the DLF 32 will live.
  • [0087]
    Simple simulations involving a few thousands of percepts 67 would require less computer resources and could be done on a high end PC, but complex simulations of higher life forms that involve millions of percepts 67 for natural language understanding could require a more powerful computer system, such as those used for large Internet servers.
  • [0088]
    A simulated or virtual environment can be made very sophisticated and is easier and less expensive than using a real one, because it can exist entirely in a computer's memory, so no external sensors or actuators are needed. To simulate high order functions such as rational consciousness accurately, a DLF 32 will eventually have to interact with the same world that human beings do, including interaction with people. However, simulations of simpler DLFs 32 do not require real world contact. Both simple and complex simulations that use external robot technologies are possible with today's technology, and will become even more realistic in the technical improvements that will come in the near future.
  • [0089]
    The present invention is based, in part, on the concept that knowledge, in order to be objective, has to be connected to reality (what is perceived). Every object has an identity which is unique, objects interact with one another. This is referred to as causality. Causality is not merely one event following another. Rather, interaction of the identities of objects is causality. There are essentially two types of objects—non living and living. Non living objects are totally externally driven. They exist unconditionally, whereas living objects exist conditionally. Certain actions they must take or they die and cease to exist. This makes them a different kind of entity, with different kind of causality. A DLF 32, according to the present invention, like a real life form, can have optional behavior. Once the DLF 32 has satisfied survival needs, it is free to do what it wants. It can engage in more survival action, can do nothing, do random actions, or the like. Alternatively, like human beings, the DLF 32 can form concepts 106—it can look around the world and learn. In both DLFs 32 and humans there are two types of behavior. The first of which is necessitated for survival which, even though “automatic” in a sense, is different from that of automatons, because it is teleological.
  • [0090]
    Since the life form, simulated or real, must maintain its survival, it must take such necessitated actions and, in order to do so it must be able to see (or sense) its environment. There will be a survival advantage in taking raw data and integrating it into percepts 67 and then into concepts 106, since the DLF 32 will be able to learn from its experience thereby, and since concepts will allow the DLF 32 to act based on generalities, and the like, thereby reducing the number of calculations required. As discussed previously herein, concept formation is among the second, optional, types of behavior which, while not immediately necessary for survival, might well enhance the likelihood of survival of the DLF 32 in the long run. Because such optional behaviors must be planned for the DLF 32 by the programmer, they will be limited in quantity as compared to a nearly infinite variety of possible optional behaviors that will be possible once the DLF 32 has formed a multitude of concepts 106. However, as discussed herein, it is important that the DLF does have at least some such optional behaviors from the start.
  • [0091]
    Since the simulated consciousness method 10 of the present invention may be readily produced and integrated with existing computer systems and sensing devices, and the like, and since the advantages as described herein are provided, it is expected that it will be readily accepted in the industry. For these and other reasons, it is expected that the utility and industrial applicability of the invention will be both significant in scope and long-lasting in duration.
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Classification aux États-Unis703/22
Classification internationaleG06N3/00, G06T13/00
Classification coopérativeG06N3/004
Classification européenneG06N3/00L
Événements juridiques
8 mars 2001ASAssignment
Effective date: 20010227
21 sept. 2001ASAssignment
Effective date: 20010907