A system for learning an external evaluation standard

Abstract: This invention pertains to a system for learning an external evaluation standard for learning the evaluation from the outside for the processing result, in a system capable of internal evaluation of the correspondence between external information and the processing result of its own system for the input information. It purports to learn the external evaluation as the internal evaluation standard of the internal evaluation time. The learning system comprises an internal evaluation means (1) for evaluation an evaluation input pattern including input information at a particular point in time and input information inputted at the next point in time for the processing result of its own system for the input information according to the internal evaluation standard at a system execution time; and an evaluation desired pattern memory means (2) for making the external evaluation correspond with the evaluation input pattern and for memorizing it as an evaluation desired pattern for having the the internal evaluation means learn the external evaluation standard. The system is configured to have an internal evaluation means to learn the evaluation desired pattern at a system's learning time.

Description: This invention relates to a learning system of a neural network. Recently, a learning system such as a neural network has been applied to alphabetical font recognition, pattern recognition such as image recognition, and control of an adaptation filter or a robot. However, in a learning system requiring teacher data, since it is difficult or even impossible to manually create teacher data, a learning system which automatically adapts to an outside environment is sought. A neural network as a learning system capable of learning a desired pattern is applied in a wide variety of fields. A hierarchical neural network of a perceptron comprises three hierarchies, e.g. an input hierarchy, an intermediate hierarchy, and an output hierarchy. The output hierarchy outputs an output pattern in response to an input pattern to the input hierarchy. To enable the network to output a proper output pattern for an input pattern, learning by the neural network is performed using a desired pattern. That is, weighting in the neural network is determined, e.g. by a back propagation method, so that a proper output pattern is presented for an input pattern and a proper output pattern is outputted. Thus, conventionally, the neural network performs its learning by using the desired pattern manually prepared beforehand. However, since in an actual application there are a pattern related to a time series, a pattern whose desired pattern itself changes and a pattern for an unpredictable state, there are problems such as that determination of the variety and quantity of the patterns prepared as desired patterns is difficult and that creation of a desired pattern is too time consuming. Hence, to create a practical learning system in an actual application, an algorithm is required that enables the learning system itself to correspond with the input pattern and the output pattern, the corresponding input pattern to be evaluated according to an internal evaluation standard maintained in its own system, and the proper input or output pattern to be learned as the desired pattern. Further, in a system capable of creating a desired pattern according to an internal evaluation standard, for a method without an interface for receiving an external evaluation for the output of the system itself, the system must be equipped with a predetermined internal evaluation standard. Therefore, when the system is applied to a variety of fields, there are problems. For example, the internal evaluation standard must be changed according to the particular application field. Thus, the system lacks flexibility. Therefore, a system is sought which has an interface for external evaluation, capable of self learning of the evaluation standard of the external evaluation. DOCUMENT INTERNATIONAL WORKSHOP OF NEURAL NETWORKS & THEIR APPLICATIONS, 13 November 1989, NEW YORK, USA; pages 407 - 427, BOCK 'The project alias: an application of collective learning systems theory to an adaptive learning image analysis system' discloses a hierarchical network of cellular automata which acquire their knowledge through learning based on a series of trial-and-error interactions with an evaluating environment. The input to the hierarchical network is provided by a set of sensors which perceive the external world. Based upon this perceived information and past experience (memory), the learning automata synthesize collections of trial responses. Periodically the automata estimate the effectiveness of these collections using either internal evaluations (unsupervised learning) or external evaluations from the environment (supervised learning), modifying their memories accordingly. This paradigm is known as Collective Learning Systems Theory. An object of the present invention is to provide a learning system capable of creating a desired pattern using an internal evaluation standard to learn an evaluation for the system's external output as the internal evaluation standard. According to the invention there is thus provided a system as set out in claim 1 for learning an external learning standard, said system having an internal evaluation standard and a function for evaluating the correspondence between external information and external output, produced from processing the external information according to input information of the internal evaluation standard, and for learning an evaluation standard of an external evaluation by using the external output and the internal evaluation standard Thus there is provided a learning system of an external evaluation standard for learning the evaluation standard of an external evaluation, used in a system having the function of evaluating the correspondence between external input information and the external output produced by processing the external input information, for taking an external evaluation from the external output into the internal evaluation standard. The present invention enables a learning system to learn the evaluation standard of the external evaluation system by using a simple external evaluation (reinforcement signal) and to use it as an internal evaluation standard of its own system. The learning system creates a desired pattern according to the internal evaluation standard, with which it learns of the processing of the external information inputted to its own system. Consequently, there is no need to replace the internal evaluation standard of the learning system from outside. Instead, by monitoring the performance of the learning system, only evaluation by a reinforcement signal needs to be supplied from outside. Therefore, when a learning system needs to be applied in various fields, the internal evaluation standard no longer needs to be replaced for each of the application fields, and a highly flexible learning system is built. This invention is applicable to any system utilizing a learning feature such as of a neural network, e.g. various robot control systems and image processing systems. Reference will now be made, by way of example, to the accompanying drawings in which:- Figure 1 is a principle block diagram of this invention; Figure 2 is a block diagram showing the system configuration of an embodiment of the learning system of this invention; Figure 3 is a block diagram showing the configuration of an embodiment of an evaluation part; Figure 4 shows an embodiment for a robot control using the learning method of this invention; Figure 5 is a diagram showing an embodiment for controlling a robot's movement locus from a starting position to a target; Figure 6 is a flowchart showing a system processing embodiment of the learning method of this invention; Figure 7 is a flowchart showing a processing embodiment of the evaluation part; Figure 8 is a diagram showing a data embodiment of an input pattern or an output pattern in a processing part; Figure 9 is a diagram showing a data embodiment of an input pattern or an output pattern in an internal evaluation part; Figure 10A and Figure 10B are diagrams for explaining embodiments of the robot's learning effect using a first external evaluation standard; and Figure 11A and Figure 11B are diagrams for explaining embodiments of the robot's learning effect using a second external evaluation standard. Figure 1 is a principle block diagram of a self learning system for learning a evaluation standard of an external evaluation, used in a learning system having a function for evaluating the correspondence between external input information and the external output produced by processing the external input information by the system itself, e.g. a neural network, according to the internal evaluation standard, for taking the external evaluation for its external output into its internal evaluation standard. In Figure 1, an internal evaluation means 1 evaluates input information, e.g. an input pattern to a neural network and a system output result of the input information, at a particular point in time during system execution, i.e. a evaluation input pattern including input information at the next point in time externally inputted for an output pattern, as evaluation object information according to an internal evaluation standard, and outputs the evaluation result, i.e. good or bad, as an evaluation signal. An evaluation desired pattern memory means 2 receives an external evaluation from the earlier described external output, has the external evaluation correspond with the evaluation input pattern, and has the internal evaluation means 1 memorize it as a desired pattern for learning the evaluation standard of the external evaluation. In Figure 1, the evaluation desired pattern memorized in the evaluation desired pattern memory means 2 at a system execution time, i.e. the evaluation input pattern and its corresponding external evaluation, is given to the internal evaluation means 1, e.g. a neural network, at a learning time or period, and the internal evaluation means 1 learns the evaluation desired pattern. Thus, the external evaluation for the system output is taken into the internal evaluation standard of the internal evaluation means 1. A system which realizes an object of this invention comprises a self learning method for learning a evaluation standard of an external evaluation, used in a learning system having the function of evaluating the correspondence between external input information, e.g. an input pattern, and the processing part of its own system for the input pattern, e.g. the external output of the processing result of the neural network, according to the internal evaluation standard. The internal evaluation means 1 performs the evaluation. The internal evaluation means 1 evaluates the evaluation input pattern by using the evaluation input pattern including the input pattern to the system at a particular point in time, e.g. time t, and the input pattern to the system at the next point in time, i.e. time t+1. What is actually evaluated is the correspondence between the system processing part, e.g. the output pattern outputted by the neural network, and the input pattern at time t. The input/output pattern at time t is temporarily memorized in the system. When the evaluation signal outputted by the internal evaluation means 1 at time t+1 is good, the input/output pattern is stored, e.g. in a desired pattern table for the system processing part. At a system learning time, the desired pattern stored in the desired pattern table is used for learning by the system processing part, e.g. the neural network. When the evaluation signal by the internal evaluation means 1 at time t+1 for the input/output pattern at time t is bad, the input/output pattern is not stored in the desired pattern table. Hence, the learning by the system processing part is performed only for the input/output pattern whose evaluation signal outputted by the internal evaluation means 1 is good. Accordingly, this invention enables the external evaluation of the system for the evaluation input pattern to be learned by the internal evaluation means 1 as the evaluation desired pattern. Figure 2 is a block diagram showing the system configuration of an embodiment of the learning system of this invention. The system comprises an input part 3 for receiving external information; a memory (STM) 4 for memorizing past external information as necessary; a processing part, e.g. a neural network, 5 for processing an input pattern created by the input part 3; a noise generating part 6 for generating noise to be added to the output of the processing part 5; an adder 7; an output part 8 for converting the output from the adder 7 to an external output; an input/output corresponding part 9 for having the input pattern outputted from the input part 3 correspond with the conforming output from the adder 7, i.e. the output pattern, and for outputting the corresponding relation as an input/output pattern; an evaluation part 10 for judging whether the input/output pattern outputted from the input/output pattern corresponding part 9 is good or bad, by using the input/output pattern outputted from the input/output corresponding part 9 and the external evaluation supplied from the outside of the system, i.e. a reinforcement signal or the like and for outputting an input/output pattern whose judging result is good; and a desired pattern table part 11 for memorizing the input/output pattern whose evaluation outputted from the evaluation part 10 is good and for outputting the input/output pattern as a desired pattern at a learning time of the processing part 5. In the embodiment of Figure 2, the noise outputted from the noise generating part 6 is added to the output from the processing part 5 for actively discovering the desired pattern for the processing part 5. Thus, the processing part 5, e.g. the neural network, always outputs the same output pattern for a particular input. To find the proper output pattern for a particular input pattern, i.e. to obtain the proper desired pattern, the noise is added to the output from the processing part 5 and the external output for the output pattern is outputted to the outside of the system, so that the system is disturbed. Since the change in the external information for the disturbance is evaluated by the internal evaluation standard described earlier, it becomes easier to search the proper output pattern, i.e. the desired pattern. Figure 3 is a configuration block diagram showing an embodiment of the evaluation part 10 shown in Figure 2. In Figure 3, the evaluation part 10 comprises an internal evaluation part 12 corresponding to the internal evaluation means 1 shown in Figure 1; an evaluation desired pattern table part 13 corresponding to the evaluation desired pattern memory means 2 shown in Figure 1; a short term memory means 14 for temporarily memorizing the input/output pattern; an evaluation input part 15 for creating the evaluation input pattern including a current input pattern and a past input pattern by using the past input/output pattern outputted from the short term memory part 14 and the input/output pattern; and an evaluation output part 16 for outputting the past input/output pattern to the desired pattern table part 11 shown in Figure 2 when the evaluation signal outputted from the internal evaluation part 12 is good. In the embodiment of Figure 2, at a system execution time, the processing part 5, e.g. the neural network, performs a processing for an input pattern created from external information inputted to the system. The output part 8 converts an output pattern, to which a noise generated by the the noise generating part 6 is added, to an external output and outputs it to the outside of the system. Concurrently, inside the system, the input/output pattern corresponding part 9 outputs the input pattern and the output pattern outputted from the adder 7 as the input/output pattern to the evaluation part 10. If the above actions are executed at time t, since the external output changes the external environment, new external information is given to the input part 3 at time t+1. By using this external information, the input/output pattern corresponding part 9 gives an input/output pattern to the evaluation part 10 at time t+1 in a similar manner to the case at time t. Meanwhile, in the embodiment of Figure 3, the input/output pattern at time t is temporarily memorized in the short term memory part 14. The input/output pattern at time t+1 and the output from the short term memory part 14, i.e. the input/output pattern at time t, are inputted to the the evaluation input part 15, which gives the evaluation input pattern including the input pattern at time t+1 and the input pattern at time t to the internal evaluation part 12 and the evaluation desired pattern table part 13. The internal evaluation part 12 evaluates the inputted evaluation input pattern according to the internal evaluation standard and outputs the evaluation signal indicating good or bad to the evaluation output part 16. When this evaluation signal is good, the evaluation output part 16 outputs the input/output pattern at time t, i.e. the past input/output pattern to the desired pattern table part 11 shown in Figure 2. The evaluation desired pattern table part 13 memorizes the correspondence between the external evaluation given from the outside of the system at time t+1, i.e. the reinforcement signal, and the evaluation input pattern. At a system learning time, in the embodiment of Figure 2, the learning of the processing part 5, e.g. the neural network, is performed by using the desired pattern stored in the desired pattern table part 11. However, in the embodiment of Figure 3, the learning of the internal evaluation part 12 is performed by using the evaluation desired pattern memorized in the evaluation desired pattern table part 13. Figure 4 shows an embodiment for a robot control using the learning method of this invention. In Figure 4, assume that a robot 17 performs a capturing action by approaching a target 18 and capturing it. The robot 17 has a visual sensor 19 and gives its output to the input part 3 shown in Figure 2 as the external information. The output part 8 converts the output pattern, i.e. the output from the processing part 5 added to the noise generated from the noise generating part 6, to the rotating angle of the robot's motor. A robot's proceeding direction angle 20 is determined according to the rotating angle, and the robot 17 moves one step in that angular direction. Figure 5 shows an embodiment for a robot's movement locus from a starting position to a target. Figure 5 shows the movement locus of the robot's step by step movement from its starting position to its target 18. In this embodiment, one trial is defined as sequences of the robot until it captures the target. A simulation described later is performed in which the trial is deemed to be a failure if the number of movement steps exceeds 100 or the robot hits a wall in the movement. Figure 5 shows the movement locus of the result. Figure 6 is a flowchart showing a system processing embodiment of the learning method of this invention. In Figure 6, when the processing starts, the input part 3 converts external information to an input pattern in S21. This input pattern is sent to the input/output corresponding part 9 in S22, and the processing part 5, e.g. the neural network, performs the processing for the input pattern in S23. Here, the processing part 5 comprises e.g. a neural network with three hierarchies. For the output from the processing part 5, the noise generating part 6 generates a noise in S24, and this noise is added to the adder 7 in S25. The output pattern to which the noise is added is sent to the input/output pattern corresponding part 9 in S26, where it corresponds with the input pattern sent to S22, and the input/output pattern corresponding part 9 outputs it as the input/output pattern in S27. The evaluation part 10 evaluates this input/output pattern according to the internal evaluation standard in S28. If the evaluation result is good, the input/output pattern is sent to the desired pattern table part 11 in S29. The output part 8 converts the output pattern outputted from the adder 7 to the external output in S30, and is outputted to the outside of the system. The external output changes the external environment in S31. In case of a robot control, for example, the output from the visual sensor 19 changes. It is judged in S32 whether any action terminating conditions, e.g. the capturing of the target, the movement steps in excess of 100 or the robot hitting the wall in case of the robot described earlier, is met. If none is met, the processes from S21 are repeated. When any of the action terminating conditions is met in S32, the learning of the processing part 5 through the back propagation method by using the desired pattern stored in the desired pattern table part 11 is performed in S33. The learning of the internal evaluation part 12 through the back propagation method by using the evaluation desired pattern stored in the evaluation desired pattern table part 13 is performed in S34, then the processings are terminated. Figure 7 is a flowchart showing a processing embodiment of the evaluation part. In Figure 7, when the processes begin, first the input/output pattern is sent from the input/output pattern corresponding part 9 in S35, and its input/output pattern is memorized in the short term memory part 14 in S36. Then in S37, it is judged whether or not the current step is the first step of the robot movement, and the processing is terminated in case of the initial step, because the evaluation input pattern described earlier cannot be obtained. In case of a non-initial step in S37, the evaluation input part 15 creates the evaluation input pattern in S38 and the internal evaluation part 12 evaluates the evaluation input pattern in S39, and the resultant evaluation signal is outputted to the evaluation output part 16 in S40. The evaluation input pattern is sent to the evaluation desired pattern table part 13 in S41. Meanwhile, the external evaluation supplied from outside the system, i.e. the reinforcement signal given to the evaluation input pattern in S42, and the external evaluation in correspondence with the evaluation input pattern is memorized in the evaluation desired pattern table part 13 in S43, and the processings are terminated. More detailed description of the earlier mentioned simulation of the learning method of this invention for the robot's actions of capturing the target is as follows. In Figure 4, the visual sensor 19 of the robot 17 has a plurality of sensor elements in a row, and the sensor element that captures the target 18 reacts. The output of the reacted sensor element is 1 and the output of the non-reacted element is 0. In the present simulation, the visual sensor 19 comprises eleven (11) sensor elements. The visual field of the visual sensor 19 is forty-five (45) degrees right and left. The visual field of one sensor element is approximately eight (8) degrees. In this embodiment, to enable a processing including a past information flow (a time series processing), an input to the processing part 5 shown in Figure 2, i.e. an input to the neural network, is not limited to the current input of the visual sensor 19, but a past sensor output is also inputted. In the present simulation, visual sensor information up to two steps before is inputted to the processing part 5. Therefore, the input hierarchy of the processing part 5 which is a three hierarchy neural network comprising an input hierarchy, an intermediate hierarchy and an output hierarchy comprises a total of thirty-three (33) units for three time series of the eleven visual sensor elements and one unit for threshold. The intermediate hierarchy comprises ten (10) units, and the output hierarchy comprises one unit for determining the robot's rotating angle. In this embodiment, since the processing part 5 is supplied with the input pattern including the past sensor output, the external information to the system is temporarily stored in the memory (STM) 4, and the current external information and the past information taken out from the memory (STM) 4 are sent to the input part 3 which is assembled into the input pattern by the input part 3 to be outputted to the processing part 5. An output pattern is obtained by adding the noise to the output from the processing part 5. The output part 8 converts the output pattern into the robot's motor rotating angle which is outputted as the external output. The present simulation is provided with a forty-five (45) degree limit right and left. Similar to the earlier description, the internal evaluation part 12 shown in Figure 3 comprises a neural network, i.e. a three-hierarchy network comprising an input hierarchy, an intermediate hierarchy and an output hierarchy. The input hierarchy comprises a total of twenty-three (23) units to which eleven (11) current visual sensor inputs, and eleven (11) visual sensor inputs of a step earlier, and a robot's motor rotating angle of a step earlier are inputted. The intermediate hierarchy comprises five units. The output hierarchy comprises a unit for determining whether the evaluation signal is good or bad. The evaluation input part 15 extracts the current sensor input, the visual sensor input of a step earlier, and the rotating angle output of a step earlier, and gives them as the evaluation input pattern to the internal evaluation part 12. The internal evaluation part 12 processes the evaluation input pattern and has the evaluation output part 16 select whether or not to have the desired pattern table part 11 memorize the input/output pattern of a step earlier memorized in the short term memory part 14 by outputting the evaluation signal. The learning of the internal evaluation standard in the internal evaluation part 12 in this case is performed by using the evaluation desired pattern stored in the evaluation desired pattern table part 13 at a learning time. Figure 8 shows a data embodiment of an input pattern or an output pattern in the processing part 5 shown in Figure 2. Figure 9 shows a data embodiment of an evaluation signal as an input pattern or an output pattern in the internal evaluation part 12 shown in Figure 3. As described earlier, the processing part 5 receives current visual sensor output A, one step earlier visual sensor output B and two steps earlier visual sensor output C, as its input patterns. Assume that the robot's starting time is step 0. Then, data B and C at step 0 are all 0. The output from the adder 7 at step 0, i.e. the output D to which a noise generated from the noise generating part 6 is 1.00, and as described earlier, this output determines the robot's direction of movement. Data E indicate an evaluation signal of the internal evaluation part 12 shown in Figure 3 for the input/output pattern composed of data D, namely, the input patterns including data A, B and C and their corresponding output patterns. This evaluation signal is actually outputted from the internal evaluation part 12 at the next step, i.e. step 1, and is an evaluation signal for the input/output pattern at step 0 whose value is shown at the position of step 0. The input/output pattern corresponding to this value being 1 is stored in the desired pattern table part 11 and learned at a learning time. That is, a learning is performed such that output pattern D is realized for input patterns A, B and C whose data E are 1. In Figure 8, the input data A at step 0 becomes data B, and the current visual sensor output at step 1 is provided as data A. As a result, output pattern D which is obtained by adding a noise to the output from the processing part 5 becomes 0.59. This value is obtained by normalizing the robot's motor rotating angle between 0 and 1. Data A at step 0 and data A at step 1 respectively become C and B at step 2, where data A are the current visual sensor output at step 2. Generally speaking, data worth a few tens of steps are similarly obtained until any of the robot's terminating conditions, i.e. capturing the target, the step number reaching 100 and hitting the wall, is met. However, for simplicity, Figure 8 only shows data as far as step 5. As described earlier, the evaluation input pattern supplied to the internal evaluation part 12 shown in Figure 9 comprises current sensor output F, one step earlier visual sensor output G, and the one step earlier output pattern from the processing part 5, i.e. output H from the adder 7, whereas the output pattern outputted from the internal evaluation part 12, i.e. the evaluation signal, is data I, and the external evaluation provided to the system from outside, i.e. the reinforcement signal, is data J. In Figure 9, data A at step 0, data A at step 1 and data D at step 0 respectively become data G, data F and data H at step 1. The evaluation input part 15 regards them as the evaluation input patterns and inputs them to the internal evaluation part 12. The internal evaluation part 12, i.e. the neural network, processes these input patterns and outputs data I as their evaluation signals which are 1.00 in this case. As described earlier, these values become data E at step 0 shown in Figure 8. The external evaluation for the external output outputted from the output part 8 at step 0 is provided to the evaluation desired pattern table part 13 as data J, and a learning is performed at a learning time such that output pattern J is realized for input patterns F, G and H. This learning is performed for all evaluation desired patterns memorized in the evaluation desired pattern table part 13. When proper output pattern J is outputted for input patterns F, G and H, however, the learning of the back propagation method is not performed for the evaluation desired pattern already studied. Because data at and after step 2 are identical to those at step 1, their explanation is omitted. In this embodiment, the robot moves from its starting position step by step, e.g. as shown in Figure 5. During one trial until the target 18 is captured after actions comprising a few tens of steps, the external evaluation for the external output of the system and its evaluation object, i.e. the evaluation input pattern, are stored in the evaluation desired pattern table part 13 as the evaluation desired pattern. When the evaluation signal of the internal evaluation part 12 for the evaluation input pattern is good, the one step earlier input/output pattern is stored as the desired pattern in the desired pattern table part 11. After termination of the robot's movement of one trial, the learning of the processing part 5 and the internal evaluation part 12 is performed. As previously stated, this learning is performed according to the back propagation method which gives the weight updating quantity, e.g. for the connecting weight W ji from unit i in the input hierarchy to unit j in the intermediate hierarchy, by the following equation. ΔW ji (n+1) = nδ j O i + αΔW ji (n) where ΔW ij (n) indicates the updating quantity at the weight updating time at the n-th time, δ j indicates the error of unit j, and O i indicates the output from unit i. The first term of the above equation is for proportioning the weight updating quantity to the weight W ji 's effect E P /w ji on the second power error E P , and the proportional constant n is called the learning constant. The larger this constant, the larger the weight updating quantity. Accordingly, the largest possible n is chosen to expedite the learning within a range in which an oscillation is not induced. The second term is an inertia term added to the weight changing equation for preventing the weight oscillation, and its constant α is called the learning speed constant. This term is for determining the magnitude of the effect by the one time earlier weight updating quantity on the current updating quantity. In the simulation in this embodiment, the learning constant values 0.03 and the learning speed constant values 0.01 are set for both the processing part 5 and the neural network of the internal evaluation part 12 in the learning. The following two standards are used for the external evaluation standards for the system's external output. First external evaluation standard: The robot approaching the target is good, and all others are bad. Second external evaluation standard: The robot approaching the target is good until it is close. When it is close, the robot approaching the target by circling the target is good. All others are bad. Figures 10A and 10B are diagrams for explaining embodiments of the robot's learning effect using the first external evaluation standard. Figure 10A shows a case in which the output pattern, to which a noise generated by the noise generating part 6 shown in Figure 2 is added at a testing time, is used for testing. Figure 10B shows a case in which the output from the processing part 5 is used "as is" as the output pattern without adding a noise. Naturally, the noise generated from the noise generating part 6 is always added at an executing time. Figures 10A and 10B respectively show a state in which a learning is not yet performed and the respective testing results after 10, 30, 50, 70, 100, 500, and 1,000 trials are terminated. At the fiftieth trial, the robot almost certainly approaches the target. In a test in which a noise is applied to the network output, the target is also approached. Figures 11A and 11B are diagrams for explaining embodiments of the robot's learning effect using a second external evaluation standard. The embodiments shown in Figures 11A and 11B utilizes the second external evaluation standard, in which the robot approaches the target. However, in the case shown in Figure 11A in which a noise is applied, generally speaking, the robot approaches the target initially, but it moves away from the target when it comes close to it. In the above embodiments, the learning of the internal evaluation part 12 and the processing part 5 are assumed to be performed after the termination of the actions of one trial by the robot. However, they could naturally be performed at each step. The input timing to the evaluation desired pattern table part 13 of the external evaluation from outside of the system can also be appropriately varied according to the actual application.

Claims: A system for learning an external evaluation standard, said system having an internal evaluation standard and a function for evaluating the correspondence between external information and external output, produced from processing the external information according to input information of the internal evaluation standard, and for learning an evaluation standard of an external evaluation by using the external output and the internal evaluation standard, said system comprising: neural network processing means (5) for processing an input pattern based on a desired pattern as input information of the internal evaluation standard, and providing an output pattern; input/output pattern comparison means (9) for comparing the input pattern with the output pattern, and for outputting a corresponding relation as an input/output pattern; internal evaluation means (10) for judging whether the input/output pattern is correct by using a reinforcement signal as the external evaluation supplied from outside of the system and the input/output pattern from the input/output pattern corresponding means, and for outputting the judged input/output pattern when the judging result is correct; and evaluation desired pattern memory means (11) including table means for storing the judged input/output pattern whose evaluation outputted from said evaluation means is correct, and for outputting the judged input/output pattern as the desired pattern during a learning period of said neural network processing means, said internal evaluation means further comprising: a short term memory (14) for temporarily storing the input/output pattern outputted from said input/output pattern corresponding means; evaluation input means (15) for creating an evaluation input pattern including a current input pattern and a past input pattern by using a current input/output pattern and a past input/output pattern outputted from said short term memory means (14); neural network internal evaluation means (12) for receiving an evaluation input pattern including information in a first period and input information at a second period which is later than the first period and for evaluating the evaluation input pattern according to the internal evaluation standard and for outputting an evaluation signal indicating whether the evaluation is correct; evaluation output means (16) for outputting the past input/output pattern stored in said short term memory means to said desired pattern table means when the evaluation signal indicates correct evaluation outputted from said neural network internal evaluation means; and evaluation desired pattern memory means (13) for receiving the reinforcement signal as the external evaluation for the external output from said output means, for making the reinforcement signal correspond with the evaluation input pattern, and for storing the evaluation input pattern as the evaluation desired pattern for having said neural network internal evaluation means learn the evaluation standard of the external evaluation. The system for learning the external evaluation standard according to claim 1 further comprising: input means (3) for receiving external information and for converting the external information to the input pattern; a memory (4) for storing past external information and for outputting the past external information to said input means; noise generating means (6) for generating noise; adder means (7) for adding an output pattern from said neural network processing means to the generated noise; output means (8) for converting noise-added output pattern from said adder to an external output, wherein an output of said adder means is supplied with said input/output pattern comparator means to compare with the input pattern. The system for learning the external evaluation standard according to claim 2, wherein said input means includes means for converting the inputted external information to the input pattern and for sending the input pattern to said input/output pattern comparison means; and wherein said internal evaluation means includes means for evaluating the input/output pattern according to the internal evaluation standard, and for sending the input/output pattern to said desired pattern table means when the evaluation result is correct; and wherein said output means includes means for converting the output pattern outputted from said adder means to the external output and for providing the external output to the outside of the system; and wherein the system includes means for changing the external environment by using the external output for judging whether the external output should be terminated due to the environmental change, for repeating actions in a later conversion of the external information to the input pattern when the external output should not be terminated and for sending the desired pattern stored in said desired pattern table means to said neural network processing means when the external output should be terminated, so that learning using a back propagation method occurs. The system for learning the external evaluation standard according to claim 3, further comprising: short term memory means for storing the sent input/output pattern at a third period according to an internal evaluation standard by said evaluation means; evaluation input means for determining whether an input of the external information is initial, for terminating processing if the external information is initial and for creating an evaluation input pattern if the external information is not initial; neural network internal evaluation means for evaluating the evaluation input pattern; evaluation output means for sending the input/output pattern in said short term memory means to the desired pattern table means when said evaluation is good; and wherein said evaluation input means includes means for providing the evaluation input pattern to the said evaluation desired pattern memory means, wherein said evaluation desired pattern memory means included means for making the reinforcement signal, as the external evaluation supplied from outside the system, correspond with the evaluation input pattern and for storing the correspondence as an evaluation desired pattern; and the evaluation desired pattern stored in said evaluation desired pattern memory means is used by said neural network internal evaluation means to perform learning using the back propagation method when the terminating condition of the external output is met. The system for learning the external evaluation standard according to claim 4 wherein said neural network processing means includes means for receiving the input pattern including a current input pattern and at least one past input pattern, obtained as a result of said input means converting the external information at a current period and the external information at least at one past period time. The system for learning the external evaluation standard according to claim 1, wherein said neural network internal evaluation means comprises means for receiving the evaluation input pattern including a current input pattern and a past input pattern, obtained as a result of said input means converting the external information at a current period and from past external information stored in said memory, as well as an output pattern stored as an output from said adder means at the same past period as the past input pattern. A system for learning the external evaluation standard according to any preceding claim, said system being connected to a robot wherein: said input means comprises means for receiving the external information which includes outputs from a plurality of visual sensors attached to the robot; and the external output indicates the direction of movement of the robot. The system for learning the external evaluation standard according to claim 3, wherein said neural network processing means includes means for receiving the input pattern including a current input pattern and at least one past input pattern obtained as a result of said input means converting the external information at least at one past period. The system for learning the external evaluation standard according to claim 8, wherein said neural network internal evaluation means comprises means for receiving the evaluation input pattern including a current input pattern and a past input pattern, said current and past input patterns being obtained as a result of said input means converting the external information at a current period and at a past time and stored in said memory, and from the output pattern from said adder means which corresponds to the past input pattern.

Inventors: NAGATA SHIGEMI, SAGA KAZUSHIGE, SEKIGUCHI MINORU, SUGASAKA TAMAMI, NAGATA, SHIGEMI, SAGA, KAZUSHIGE, SEKIGUCHI, MINORU, SUGASAKA, TAMAMI
Applicants: FUJITSU LTD, FUJITSU LIMITED
Application year: 1990
Main classification: G06F15
IPC codes: G06F15, G06N99