1. Field of the Invention
The present invention generally relates to a genetic robot, and in particular, to a system and method for generating a genetic code of a genetic robot according to a user input.
2. Description of the Related Art
In general, the phrase ‘genetic robot’ refers to an artificial creature, a software robot (Sobot), or a general robot having a unique genetic code. A robot genetic code generally refers to a single robot genome composed of a plurality of artificial chromosomes. A Sobot generally refers to a software artificial creature which can interact with a user as an independent software agent or operate as an intelligent unit of a hardware robot, which links a sensor network and the hardware robot, while moving across networks.
A plurality of artificial chromosomes embodied in a robot genome define transition between internal states of the robot, such as motivation, homeostasis, and emotion, and a personality of the robot deciding behavior of the robot aroused by the transition, while interacting with the environment external to the robot. The definition of artificial creature, personality, motivation, homeostasis, emotion, and behavior is shown in Table 1.
TABLE 1ArtificialArtificial handwork, which acts according to self-moti-creaturevation, has emotion, interacts with a human being in real-time, and selects behavior.PersonalityNot description obtained by simply summarizing behaviorbut a determiner of a partial or entire portion. It can beanalyzed as human personality. Concept includingmotivation, homeostasis, and emotion. That is, a personalityengine means an engine having motivation, homeostasis,and emotion. Determiner generating various types ofinternal states and behaviors.MotivationProcess of arousing and maintaining behaviors of anorganism and controlling a pattern of the behaviors. Reasonfor selecting and performing behavior, e.g., curiosity,intimacy, monotony, avoidance, greed, the desire to control,etc.HomeostasisFunction of maintaining a physiological state as anindividual in a stable state even when an organism contin-uously suffers a change of an external and internalenvironment. Reason for selecting and performing behavior,e.g., hunger, drowsiness, fatigue, etc.EmotionSubjective restlessness occurring when an organismperforms a certain behavior. For example, happiness,sadness, anger, fear, etc.BehaviorGeneric term indicating that an individual moves to aspecific point or stops. Sleeping, eating, and running areexamples in the case of an animal. The number ofbehaviors, which can be selected by an individual, is finite,and each individual can perform only one behavior at acertain time.
Artificial chromosomes can be classified into essential element related genes, internal state related genes, and behavior decision related genes. Essential element related genes are essential parameters significantly affecting an internal state change and an outwardly manifested behavior, internal state related genes are parameters affecting internal states of a robot in associated with an external input applied to the robot, and behavior decision related genes are parameters used to decide an outwardly manifested behavior according to currently determined internal states.
Internal states include states of motivation, homeostasis, emotion, etc. That is, internal states of a robot can be determined by internal states and parameters of internal states according to external stimuli, i.e. internal state related genes, as shown in Table 2.
TABLE 2Internal stateExternalMotivationHomeostasisEmotionstimulusIntimacy. . .HostilityHunger. . .DrowsinessHappiness. . .SadnessPatting80. . .−400. . .040. . .−20Hitting−30. . .500. . .0−30. . .30Surprising0. . .50. . .010. . .0. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .Soothing40. . .−400. . .050. . .−50
Behavior decision related genes can be determined as well, except that external stimuli are replaced with various behaviors. That is, for behavior decision related genes, various parameters corresponding to specific behaviors are contained for each internal state. For example, when essential element related genes are composed of volatility, initial value, mean value, convergence value, temporal decay value, and specific value specified by a specific time, as essential parameters significantly affecting an internal state change and an outwardly manifested behavior, the essential element related genes can contain volatility, initial value, mean value, convergence value, decay value, and specific value according to internal states of motivation, homeostasis, and emotion. As described above, a robot genome includes essential element related genes composed of parameters of internal states and elements essential to an internal state change and an outwardly manifested behavior corresponding to each internal state, internal state related genes composed of parameters of various external stimuli and internal states corresponding to each external stimulus, and behavior decision related genes composed of parameters of various manifested behaviors and internal states corresponding to each manifested behavior. That is, a robot genome can be represented by a two dimensional matrix of the internal states, and essential elements, external stimuli, and manifested behaviors corresponding to each internal state, as shown in Table 3.
TABLE 3MotivationHomeostasisEmotionIntimacy. . .HostilityHunger. . .DrowsinessHappiness. . .SadnessEssentialVolatilityEssential elementEssential elementEssential elementelementInitialrelated genesrelated genesrelated genesvalue(motivation)(homeostasis)(emotion). . .DecayvalueExternalPattingInternal stateInternal state relatedInternal statestimulusHittingrelated genesgenes (homeostasis)related genes. . .(motivation)(emotion)SoothingManifestedSmilingBehavior decisionBehavior decisionBehavior decisionbehaviorLookingrelated genesrelated genesrelated genesaround(motivation)(homeostasis)(emotion). . .Rolling
Thus, as the number of attributes of each internal state is larger, and as the number of external stimuli, behavior decisions, and essential elements is larger, a robot can have a larger number of genes, representing a larger variety of behaviors. However, a user directly inputs parameters of these genes. For example, if the number of internal state attributes is 14, the number of essential elements is 5, the number of external stimuli is 47, and the number of manifested behaviors is 77, the number of parameters that are input by the user, i.e. the number of genes, reaches 70 (5×14) essential element related genes, 658 (47×14) internal state related genes, and 1078 (77×14) behavior decision related genes, i.e. a total of 1806 (=70+658+1078). Thus, in this case, it is difficult for the user to directly input this number of parameters.
In addition, even if the user directly inputs the parameters, the user has no way to determine whether a personality of the robot manifested according to the parameters input by the user is desired by the user. That is, the user cannot know a personality of the robot before the personality is manifested, and even if the personality is manifested, the user has no way to test whether the robot has a personality desired by the user.
In order to address these problems, robots are currently available by setting all genes as a uniform value so each robot can have a personality desired by a user through interacting with and learning from the user (the former). Alternatively, robot personality models may be pre-made so each user can select one of various pre-made personality models for his/her robot (the latter). The former has a problem because of the length of time needed to make a robot learn a personality desired by a user, and the latter has a problem because robots having the same personality model have the same personality even when a personality model is verified and that specific personality is manifested, since the personality of each robot cannot be specified according to a user. That is, existing robot genome generation methods cannot solve conventional problems occurring when a robot genome is generated.