# US 2019/0317455 A1 · Methods and apparatus to generate acceptability criteria for autonomous systems plans > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2019/0317455 A1 - Document ID: US20190317455A1 - Status: Published application - Kind: A1 - Application number: 16/456,957 - Filed: 2019-06-28 - Published: 2019-10-17 - Earliest claimed: 2019-06-28 - Assignees: Intel Corporation - Applicants: Intel Corporation - Inventors: Javier Felip Leon; Javier Sebastian Turek; David I. Gonzalez Aguirre; Ignacio Javier Alvarez; Luis Carlos Maria Remis; Justin Gottschlich - Web page: https://patentgrep.com/patent/US20190317455A1 ## CPC - G05B 13/027: {using neural networks only} - G05B 13/04: involving the use of models or simulators - G06N 3/042: Knowledge-based neural networks; Logical representations of neural networks - G06N 3/044: Recurrent networks, e.g. Hopfield networks - G06N 3/0442: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] - G06N 3/045: Combinations of networks - G06N 3/0455: Auto-encoder networks; Encoder-decoder networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/08: Learning methods - G06N 3/082: modifying the architecture, e.g. adding, deleting or silencing nodes or connections - G06N 3/086: using evolutionary algorithms, e.g. genetic algorithms or genetic programming - G06N 3/088: Non-supervised learning, e.g. competitive learning - G06N 3/0895: Weakly supervised learning, e.g. semi-supervised or self-supervised learning - G06N 3/09: Supervised learning - G06N 3/092: Reinforcement learning - G06N 3/096: Transfer learning - G06N 5/025: Extracting rules from data - G06N 20/00: Machine learning - G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Apparatus, systems, articles of manufacture, and methods to generate acceptability criteria for autonomous systems plans are disclosed. An example apparatus includes a data compiler to compile data generated by the autonomous system into an autonomous system task dataset, a data encoder to encode the dataset for input into a rule distillation neural network architecture, a model trainer to train the rule distillation neural network architecture, an adaptor to adapt the trained rule distillation neural network architecture to a new input data domain using the autonomous system task dataset, a verifier to generate formally verified acceptability criteria, and an inferer to evaluate a control command, the evaluation resulting in an acceptance or rejection of the command. ## Claims ### Claim 1 An apparatus for validating commands of an autonomous system, the apparatus comprising: a data compiler to compile data generated by the autonomous system into an autonomous system task dataset; a data encoder to encode the dataset for input into a rule distillation neural network architecture; a model trainer to train the rule distillation neural network architecture; an adaptor to adapt the trained rule distillation neural network architecture to a new input data domain using the autonomous system task dataset; a verifier to generate formally verified acceptability criteria; and an inferer to evaluate a control command, the evaluation resulting in an acceptance or rejection of the command. ### Claim 2 (depends on 1) The apparatus of claim 1, wherein the verifier generates formally verifiable criteria for at least one of an embodiment, a task, a sensed state, or a control command. ### Claim 3 (depends on 2) The apparatus of claim 2, further including: a task planner to generate a sequence of control commands, the task planner to receive at least one of an embodiment description or a task description; and a simulator to generate synthetic sensor data from a simulated environment, the simulator to receive the sequence of control commands generated by the task planner. ### Claim 4 (depends on 1) The apparatus of claim 1, wherein the adaptor includes a self-supervised adaptation mode to modify the neural network architecture offline. ### Claim 5 (depends on 1) The apparatus of claim 1, wherein the new input data domain includes at least one of a new embodiment description or a new task description. ### Claim 6 (depends on 1) The apparatus of claim 1, wherein the acceptability criteria are at least one of a safety related or a performance related criteria. ### Claim 7 (depends on 1) The apparatus of claim 1, wherein evaluation of a control command is used to determine if the control command is acceptable under at least one of a current state of the system, a current task, or a current environment. ### Claim 8 (depends on 3) The apparatus of claim 3, wherein the adaptor is to train the system using the simulator and the task planner. ### Claim 9 (depends on 1) The apparatus of claim 1, wherein at least one of the model trainer or adaptor include a cost function, the cost function iteratively optimized during the training, the training completed when the cost function is converged. ### Claim 10 (depends on 1) The apparatus of claim 1, wherein the inferer is to evaluate a control command using an on-line inference mode. ### Claim 11 (depends on 1) The apparatus of claim 1, wherein the encoder is to encode: an embodiment, the embodiment input in a mark-up language, the mark-up language encoded into a numerical representation using word embedding, a recurrent neural network to output an encoded embodiment; and a task, the task encoded including at least one of unidimensional sensor values or multidimensional sensor values, wherein the unidimensional sensor values are processed as a sequence of pairs by a recurrent neural network, wherein the multidimensional sensor values are normalized and encoded using the recurrent neural network and a convolutional neural network. ### Claim 12 (depends on 1) The apparatus of claim 1, wherein the rule distillation network architecture is to: concatenate the encoded input, the input fed to a recurrent neural network; output a hidden state, the hidden state converted by a MultiLayer Perceptron (MLP) to a distribution over multiple rule statements; and output, for the rule statement, a sensor identifier, a sensor value, and an instruction, the outputs forming a rule list for formal verification by the verifier. ### Claim 13 (depends on 1) The apparatus of claim 1, wherein the verifier includes a temporal logic requirement used to calculate a correctness measure for a reachability analysis used during the neural network training. ### Claim 14 A method of validating commands of an autonomous system, the method comprising: compiling data generated by the autonomous system into an autonomous system task dataset; encoding the dataset for input into a rule distillation neural network architecture; training the rule distillation neural network architecture; modifying the rule distillation neural network architecture by adapting it to a new input data domain, the autonomous system task dataset used to train the modified neural network architecture; generating formally verified acceptability criteria; and evaluating a control command, the evaluation to result in an acceptance or rejection of the command. ### Claim 15 (depends on 14) The method of claim 14, wherein the modifying of the rule distillation neural network architecture includes generating a sequence of control commands and synthetic sensor data from a simulated environment. ### Claim 16 (depends on 14) The method of claim 14, wherein the formal verification of the acceptability criteria includes a reachability analysis for continuous measure of a robustness of an execution plan to determine constraints towards rules learned by the system. ### Claim 17 (depends on 14) The method of claim 14, wherein training the rule distillation neural network includes feeding encoded data inputs into a recurrent neural network, the recurrent neural network generating logic rule statements for use in a rule set. ### Claim at position 18 18-21. (canceled) ### Claim 22 (depends on 14) The method of claim 14, wherein the encoding includes: encoding an embodiment, the embodiment input in a mark-up language, the mark-up language encoded into a numerical representation using word embedding, a recurrent neural network to output an encoded embodiment; and encoding a task, the task encoded including at least one of unidimensional sensor values or multidimensional sensor values, wherein the unidimensional sensor values are processed as a sequence of pairs by a recurrent neural network, wherein the multidimensional sensor values are normalized and encoded using the recurrent neural network and a convolutional neural network. ### Claim 23 A non-transitory computer readable storage medium comprising computer readable instructions that, when executed, cause one or more processors to, at least: compile data generated by an autonomous system into an autonomous system task dataset; encode the dataset for input into a rule distillation neural network architecture; train the rule distillation neural network architecture; modify the rule distillation neural network architecture by adapting it to a new input data domain, the autonomous system task dataset used to train the modified neural network architecture; generate formally verified acceptability criteria; and evaluate a control command, the evaluation resulting in an acceptance or rejection of the command. ### Claim 24 (depends on 23) The storage medium of claim 23, wherein the instructions further cause the one or more processors to generate a sequence of control commands and synthetic sensor data from a simulated environment. ### Claim 25 (depends on 23) The storage medium of claim 23, wherein the instructions, when executed, cause the one or more processors to feed encoded data inputs into a recurrent neural network, a recurrent neural network generating logic rule statements for use in a rule set. ### Claim 26 (depends on 23) The storage medium of claim 23, wherein the instructions, when executed, cause the one or more processors to generate a rule set for each of a command state and a sensed state input data, iterate until a cost function is converged, and train the rule distillation neural network before a new command is executed, the new input data to be provided at every iteration of a control loop. ### Claim 27 (depends on 23) The storage medium of claim 23, wherein the instructions, when executed, cause the one or more processors to determine if the control command is acceptable under at least one of a current state of the system, a current task, or a current environment. ### Claim at position 25 28-30. (canceled) ### Claim 31 An apparatus for validating commands of an autonomous system, the apparatus comprising: means for compiling data generated by the autonomous system into an autonomous system task dataset; means for encoding the dataset for input into a rule distillation neural network architecture; means for training the rule distillation neural network architecture; means for modifying the rule distillation neural network architecture by adapting it to a new input data domain, the autonomous system task dataset used to train the modified neural network architecture; a first means for generating formally verified acceptability criteria; and means for evaluating a control command, the evaluation resulting in an acceptance or rejection of the command. ### Claim 32 (depends on 31) The apparatus of claim 31, further including: a second means for generating a sequence of control commands, the means for generating a sequence of control commands to receive at least one of an embodiment description or a task description; and a third means for generating synthetic sensor data from a simulated environment, the means for generating to receive the sequence of control commands generated by the means for generating a sequence of control commands. ### Claim at position 28 33-38. (canceled)