# US 12,769,979 B2 · Artificial intelligence planning method and artificial intelligence planning device

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- Number: US 12,769,979 B2
- Document ID: US12769979B2
- Status: Granted
- Kind: B2
- Application number: 17/530,479
- Filed: 2021-11-19
- Published: 2023-04-13
- Granted: 2026-10-20
- Earliest claimed: 2021-10-13
- Assignees: Wistron Corporation
- Applicants: Wistron Corporation
- Inventors: Chih-Ming Chen
- Web page: https://patentgrep.com/patent/US12769979B2

## CPC

- G06N 3/04: Architecture, e.g. interconnection topology
- H04L 41/145: {involving simulating, designing, planning or modelling of a network}
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)

## Abstract

An artificial intelligence planning method and an artificial intelligence planning device are provided. When a considered specific application has a plurality of planning layers, a corresponding planning node set and a planning tree are built for each of the planning layers according to a domain file, and all planning nodes in the planning node set of each of the planning layers satisfy a plurality of corresponding preset conditions. In addition, a corresponding planning path may be determined in each of the planning trees, and a plurality of action commands may be accordingly issued to the specific application to achieve a corresponding target state. In this way, the specific application having multiple planning layers may be equipped with self-configuration, self-optimizing, and self-healing functions and is then implemented as a self-organizing application.

## Claims

### Claim 1

An artificial intelligence planning method, executed by an artificial intelligence planning device comprising a processor, comprising:
obtaining, by an artificial intelligence planner executed by the processor, a problem file and a domain file associated with a specific application run on a target server, wherein the domain file comprises N description segments corresponding to N planning layers of the specific application, N is a positive integer greater than 1, and the problem file indicates a first initial state and a target state of each of the planning layers;
constructing, by the artificial intelligence planner executed by the processor, N planning trees corresponding to the planning layers based on the description segments, wherein each of the planning trees has a plurality of state nodes and corresponds to one of the planning layers, wherein each of the planning layers has a planning node set, the planning node set of each of the planning layers comprises a plurality of planning nodes, the planning nodes of an ith planning layer among the planning layers comprise a first planning node, the planning nodes of an i+1th planning layer among the planning layers comprise a plurality of second planning nodes corresponding to the first planning node, the first planning node satisfies a plurality of first preset conditions corresponding to the ith planning layer, and all of the planning nodes of a second planning tree satisfy a plurality of second preset conditions of the i+1th layer, wherein i is a positive integer and N>i≥1;
determining, by the artificial intelligence planner executed by the processor, a first planning path in the state nodes of each of the planning trees based on a deep learning model, wherein the first planning path of each of the planning trees guides the corresponding planning layer from the corresponding first initial state to the corresponding target state; and
generating, by the artificial intelligence planner executed by the processor, a first scheduling plan according to the first planning path of a specific planning tree among the planning trees, wherein the first scheduling plan comprises a plurality of action commands assigned with corresponding execution time point;
sequentially issuing, by the artificial intelligence planner executed by the processor, the plurality of action commands to the specific application run on the target server according to the first scheduling plan, wherein the plurality of the action commands controls the target server to add a piece of program code to a program code of the specific application or remove a piece of program code from the program code of the specific application at the corresponding execution time point;
wherein the description segments comprise an Nth description segment corresponding to an Nth planning layer among the planning layers, the Nth description segment comprises a plurality of description sentences corresponding to the Nth planning layer, and each of the description sentences is expressed as follows:
precond_N->action N->effect_N
wherein precond_N is a pre-state, action_N is a specific action command, and effect_N is a result state which is achievable by executing the specific action command in the pre-state,
wherein action_N comprises adding a node to the planning nodes of the Nth planning layer or removing a node from the planning nodes of the Nth planning layer, and effect_N is the planning node set formed by the planning nodes of the Nth planning layer after the specific action command is executed in the pre-state, the Nth planning layer corresponds to a code layer of the specific application, and action_N comprises adding the piece of program code to the program code of the specific application or removing the piece of program code from the program code of the specific application,
wherein the description segments comprise an ith description segment corresponding to the ith planning layer, the ith description segment comprises a plurality of first description sentences corresponding to the ith planning layer, and each of the first description sentences is expressed as follows:
precond_i->action_i->effect_i,
wherein precond_i is a pre-state, action_i is a specific action command, and effect_i is a result state which may be achieved by executing the specific action command in the pre-state;
wherein the first planning node corresponds to one of the first description sentences, and the method further comprises:
determining whether all of the second planning nodes satisfy the second preset conditions corresponding to the i+1th layer before executing action_i corresponding to the first planning node; and
executing action_i corresponding to the first planning node in response to determining that all of the second planning nodes satisfy the second preset conditions corresponding to the i+1th layer, otherwise, not executing action_i corresponding to the first planning node;
wherein the first preset conditions met by the first planning node comprise achieving a metric of the ith planning layer, and the method further comprises the following steps:
obtaining input data of the specific application, determining a plurality of reference patterns among a plurality of patterns corresponding to the ith planning layer in response to the input data by executing an input attention function, determining a plurality of feature values corresponding to the plurality of reference patterns by executing conscious attention function and estimating a fitness score corresponding to the ith planning layer according to the plurality of feature values; and
determining that the first planning node achieves the metric of the ith planning layer in response to determining that the fitness score corresponding to the ith planning layer is greater than a score threshold of the ith planning layer,
wherein the step of estimating the fitness score corresponding to the ith planning layer according to the input data further comprises:
executing an input attention function to find a plurality of reference patterns among a plurality of patterns corresponding to the ith planning layer in response to the input data;
executing a conscious attention function to find a plurality of feature values corresponding to the reference patterns; and
determining the fitness score corresponding to the ith planning layer based on the feature values.

### Claim 2 (depends on 1)

The method according to claim 1, wherein the first preset conditions met by the first planning node comprise that the planning node set of the ith planning layer is not empty and comprise meeting a requirement of the ith planning layer and achieving a metric of the ith planning layer.

### Claim 3 (depends on 2)

The method according to claim 2, wherein the requirement of the ith planning layer is determined based on a control flow graph, a data flow graph, a call graph, and a constraint condition graph.

### Claim 4 (depends on 1)

The method according to claim 1, wherein the planning trees comprise a first planning tree corresponding to an ith planning tree, and the step of determining the first planning path in the state nodes of each of the planning trees based on the deep learning model further comprises:
selecting, by the deep learning model, at least one reference state node following a first state node among the state nodes of the first planning tree based on an operation observation associated with the specific application and a state of the first state node regarding the first state node of the first planning tree;
obtaining the at least one reference state node corresponding to each of the state nodes of the first planning tree and connecting each of the state nodes of the first planning tree and the corresponding at least one reference state node to form at least one candidate path in the first planning tree, wherein the at least one candidate path guides the ith planning layer from the corresponding first initial state to the corresponding target state; and
treating one of the at least one candidate path of the first planning tree as the first planning path of the first planning tree.

### Claim 5 (depends on 1)

The method according to claim 1, wherein action_i comprises adding a node to the planning nodes of the ith planning layer or removing a node from the planning nodes of the ith planning layer, and effect_i is the planning node set formed by the planning nodes of the ith planning layer after the specific action command is executed in the pre-state.

### Claim 6 (depends on 1)

The method according to claim 1, wherein the specific planning tree corresponds to an Nth planning layer among the planning layers, the Nth planning layer corresponds to a code layer of the specific application, and each of the action commands comprises adding a piece of program code to the specific application or removing a piece of program code from the specific application.

### Claim 7 (depends on 1)

The method according to claim 1, further comprising:
in response to determining that a first action command among the action commands is not successfully executed, returning to a previous state and executing the first action command again.

### Claim 8 (depends on 1)

The method according to claim 1, wherein the first planning path of the specific planning tree is an unscheduled plan comprising the action commands, and the step of generating the first scheduling plan according to the first planning path of the specific planning tree among the planning trees further comprises:
assigning, by a scheduler, a corresponding execution time point and an execution mode to each of the action commands in the unscheduled plan to generate the first scheduling plan,
wherein the step of sequentially issuing the action commands to the specific application further comprises:
sequentially issuing the action commands to the specific application according to the execution time point of each of the action commands.

### Claim 9 (depends on 1)

The method according to claim 1, further comprising:
obtaining an operation observation generated by the specific application in response to a first action command among the action commands;
converting the operation observation of the specific application into an execution state;
determining a second initial state based on the execution state of the specific application in response to determining that the execution state of the specific application indicates that a new scheduling plan is required to be generated and determining a second planning path in the state nodes of each of the planning trees based on the deep learning model, wherein the second planning path of each of the planning trees guides the corresponding planning layer from the corresponding second initial state to the corresponding target state; and
generating a second scheduling plan according to the second planning path of the specific planning tree among the planning trees and sequentially issuing a plurality of other action commands to the specific application accordingly.

### Claim 10

An artificial intelligence planning device, comprising:
a storage circuit, storing a program code;
a processor, coupled to the storage circuit, accessing the program code for:
obtaining a problem file and a domain file associated with a specific application run on a target server, wherein the domain file comprises N description segments corresponding to N planning layers of the specific application, N is a positive integer greater than 1, and the problem file indicates a first initial state and a target state of each of the planning layers;
constructing N planning trees corresponding to the planning layers based on the description segments, wherein each of the planning trees has a plurality of state nodes and corresponds to one of the planning layers, wherein each of the planning layers has a planning node set, the planning node set of each of the planning layers comprises a plurality of planning nodes, the planning nodes of an ith planning layer among the planning layers comprise a first planning node, the planning nodes of an i+1th planning layer among the planning layers comprise a plurality of second planning nodes corresponding to the first planning node, the first planning node satisfies a plurality of first preset conditions corresponding to the ith planning layer, and all of the planning nodes of a second planning tree satisfy a plurality of second preset conditions of the i+1th layer, wherein i is a positive integer and N>i≥1;
determining a first planning path in the state nodes of each of the planning trees based on a deep learning model, wherein the first planning path of each of the planning trees guides the corresponding planning layer from the corresponding first initial state to the corresponding target state; and
generating a first scheduling plan according to the first planning path of a specific planning tree among the planning trees, wherein the first scheduling plan comprises a plurality of action commands assigned with corresponding execution time point;
sequentially issuing the plurality of action commands to the specific application run on the target server according to the first scheduling plan, wherein the plurality of the action commands controls the target server to add a piece of program code to a program code of the specific application or remove a piece of program code from the program code of the specific application at the corresponding execution time point;
wherein the description segments comprise an Nth description segment corresponding to an Nth planning layer among the planning layers, the Nth description segment comprises a plurality of description sentences corresponding to the Nth planning layer, and each of the description sentences is expressed as follows:
precond_N->action_N->effect_N
wherein precond_N is a pre-state, action_N is a specific action command, and effect_N is a result state which is achievable by executing the specific action command in the pre-state,
wherein action_N comprises adding a node to the planning nodes of the Nth planning layer or removing a node from the planning nodes of the Nth planning layer, and effect_N is the planning node set formed by the planning nodes of the Nth planning layer after the specific action command is executed in the pre-state, the Nth planning layer corresponds to a code layer of the specific application, and action_N comprises adding the piece of program code to the program code of the specific application or removing the piece of program code from the program code of the specific application,
wherein the description segments comprise an ith description segment corresponding to the ith planning layer, the ith description segment comprises a plurality of first description sentences corresponding to the ith planning layer, and each of the first description sentences is expressed as follows:
precond_i->action_i->effect_i,
wherein precond_i is a pre-state, action_i is a specific action command, and effect_i is a result state which may be achieved by executing the specific action command in the pre-state;
wherein the first planning node corresponds to one of the first description sentences, and the processor is configured to further perform:
determining whether all of the second planning nodes satisfy the second preset conditions corresponding to the i+1th layer before executing action_i corresponding to the first planning node; and
executing action_i corresponding to the first planning node in response to determining that all of the second planning nodes satisfy the second preset conditions corresponding to the i+1th layer, otherwise, not executing action_i corresponding to the first planning node;
wherein the first preset conditions met by the first planning node comprise achieving a metric of the ith planning layer, and the method further comprises the following steps:
obtaining input data of the specific application, determining a plurality of reference patterns among a plurality of patterns corresponding to the ith planning layer in response to the input data by executing an input attention function, determining a plurality of feature values corresponding to the plurality of reference patterns by executing conscious attention function and estimating a fitness score corresponding to the ith planning layer according to the plurality of feature values; and
determining that the first planning node achieves the metric of the ith planning layer in response to determining that the fitness score corresponding to the ith planning layer is greater than a score threshold of the ith planning layer,
wherein the step of estimating the fitness score corresponding to the ith planning layer according to the input data further comprises:
executing an input attention function to find a plurality of reference patterns among a plurality of patterns corresponding to the ith planning layer in response to the input data;
executing a conscious attention function to find a plurality of feature values corresponding to the reference patterns; and
determining the fitness score corresponding to the ith planning layer based on the feature values.

### Claim 11

An artificial intelligence planning method, comprising:
obtaining a problem file and a domain file associated with a specific application run on a target server, wherein the domain file comprises N description segments corresponding to N planning layers of the specific application, N is a positive integer greater than or equal to 1, and the problem file indicates a first initial state and a target state of each of the planning layers;
constructing N planning trees corresponding to the planning layers based on the description segments, wherein each of the planning trees has a plurality of state nodes and corresponds to one of the planning layers;
determining a first planning path in the state nodes of each of the planning trees based on a deep learning model, wherein the first planning path of each of the planning trees guides the corresponding planning layer from the corresponding first initial state to the corresponding target state;
generating a first scheduling plan according to the first planning path of a specific planning tree among the planning trees, wherein the first scheduling plan comprises a plurality of action commands assigned with corresponding execution time point;
sequentially issuing the plurality of action commands to the specific application run on the target server according to the first scheduling plan wherein the plurality of the action commands controls the target server to add a piece of program code to a program code of the specific application or remove a piece of program code from the program code of the specific application at the corresponding execution time point;
obtaining an operation observation generated by the specific application in response to a first action command among the action commands;
converting the operation observation of the specific application into an execution state;
determining a second initial state based on the execution state of the specific application in response to determining that the execution state of the specific application indicates that a new scheduling plan is required to be generated and determining a second planning path in the state nodes of each of the planning trees based on the deep learning model, wherein the second planning path of each of the planning trees guides the corresponding planning layer from the corresponding second initial state to the corresponding target state; and
generating a second scheduling plan according to the second planning path of the specific planning tree among the planning trees and sequentially issuing a plurality of other action commands to the specific application accordingly,
wherein the description segments comprise an Nth description segment corresponding to an Nth planning layer among the planning layers, the Nth description segment comprises a plurality of description sentences corresponding to the Nth planning layer, and each of the description sentences is expressed as follows:
precond_N->action_N->effect_N,
wherein precond_N is a pre-state, action_N is a specific action command, and effect_N is a result state which may be achieved by executing the specific action command in the pre-state,
wherein action_N comprises adding a node to the planning nodes of the Nth planning layer or removing a node from the planning nodes of the Nth planning layer, and effect_N is a planning node set formed by the planning nodes of the Nth planning layer after the specific action command is executed in the pre-state, the Nth planning layer corresponds to a code layer of the specific application, and action_N comprises adding the piece of program code to the program code of the specific application or removing the piece of program code from the program code of the specific application,
wherein the description segments comprise an ith description segment corresponding to the ith planning layer, the ith description segment comprises a plurality of first description sentences corresponding to the ith planning layer, and each of the first description sentences is expressed as follows:
precond_i->action_i->effect_i,
wherein precond_i is a pre-state, action_i is a specific action command, and effect_i is a result state which may be achieved by executing the specific action command in the pre-state;
wherein the first planning node corresponds to one of the first description sentences, and the method further comprises:
determining whether all of the second planning nodes satisfy the second preset conditions corresponding to the i+1th layer before executing action_i corresponding to the first planning node; and
executing action_i corresponding to the first planning node in response to determining that all of the second planning nodes satisfy the second preset conditions corresponding to the i+1th layer, otherwise, not executing action_i corresponding to the first planning node;
wherein the first preset conditions met by the first planning node comprise achieving a metric of the ith planning layer, and the method further comprises the following steps:
obtaining input data of the specific application, determining a plurality of reference patterns among a plurality of patterns corresponding to the ith planning layer in response to the input data by executing an input attention function, determining a plurality of feature values corresponding to the plurality of reference patterns by executing conscious attention function and estimating a fitness score corresponding to the ith planning layer according to the plurality of feature values; and
determining that the first planning node achieves the metric of the ith planning layer in response to determining that the fitness score corresponding to the ith planning layer is greater than a score threshold of the ith planning layer,
wherein the step of estimating the fitness score corresponding to the ith planning layer according to the input data further comprises:
executing an input attention function to find a plurality of reference patterns among a plurality of patterns corresponding to the ith planning layer in response to the input data;
executing a conscious attention function to find a plurality of feature values corresponding to the reference patterns; and
determining the fitness score corresponding to the ith planning layer based on the feature values.

### Claim 12 (depends on 11)

The method according to claim 11, wherein each of the planning layers has a planning node set, the planning node set of each of the planning layers comprises a plurality of planning nodes, the planning nodes of an ith planning layer among the planning layers comprise a first planning node, the planning nodes of an i+1th planning layer among the planning layers comprise a plurality of second planning nodes corresponding to the first planning node, the first planning node satisfies a plurality of first preset conditions corresponding to the ith planning layer, and all of the planning nodes of a second planning tree satisfy a plurality of second preset conditions of the i+1th layer, wherein i is a positive integer and N>i≥1.

### Claim 13 (depends on 12)

The method according to claim 12, wherein the first preset conditions met by the first planning node comprise that the planning node set of the ith planning layer is not empty and comprise meeting a requirement of the ith planning layer and achieving a metric of the ith planning layer.

### Claim 14 (depends on 13)

The method according to claim 13, wherein the requirement of the ith planning layer is determined based on a control flow graph, a data flow graph, a call graph, and a constraint condition graph.

### Claim 15 (depends on 13)

The method according to claim 13, wherein the first preset conditions met by the first planning node comprise achieving a metric of the ith planning layer, and the method further comprises the following steps:
obtaining input data of the specific application and estimating a fitness score corresponding to the ith planning layer according to the input data; and
determining that the first planning node achieves the metric of the ith planning layer in response to determining that the fitness score corresponding to the ith planning layer is greater than a score threshold of the ith planning layer,
wherein the step of estimating the fitness score corresponding to the ith planning layer according to the input data further comprises:
executing an input attention function to find a plurality of reference patterns among a plurality of patterns corresponding to the ith planning layer in response to the input data;
executing a conscious attention function to find a plurality of feature values corresponding to the reference patterns; and
determining the fitness score corresponding to the ith planning layer based on the feature values.

### Claim 16 (depends on 11)

The method according to claim 11, wherein the planning trees comprise a first planning tree corresponding to an ith planning tree, and the step of determining the first planning path in the state nodes of each of the planning trees based on the deep learning model further comprises:
selecting, by the deep learning model, at least one reference state node following a first state node among the state nodes of the first planning tree based on an operation observation associated with the specific application and a state of the first state node regarding the first state node of the first planning tree;
obtaining the at least one reference state node corresponding to each of the state nodes of the first planning tree and connecting each of the state nodes of the first planning tree and the corresponding at least one reference state node to form at least one candidate path in the first planning tree, wherein the at least one candidate path guides the ith planning layer from the corresponding first initial state to the corresponding target state; and
treating one of the at least one candidate path of the first planning tree as the first planning path of the first planning tree.

### Claim 17 (depends on 11)

The method according to claim 11, wherein action_i comprises adding a node to the planning nodes of the ith planning layer or removing a node from the planning nodes of the ith planning layer, and effect_i is a planning node set formed by the planning nodes of the ith planning layer after the specific action command is executed in the pre-state.

### Claim 18 (depends on 11)

The method according to claim 11, wherein the specific planning tree corresponds to an Nth planning layer among the planning layers, the Nth planning layer corresponds to a code layer of the specific application, and each of the action commands comprises adding a piece of program code to the specific application or removing a piece of program code from the specific application.

### Claim 19 (depends on 11)

The method according to claim 11, further comprising:
in response to determining that a first action command among the action commands is not successfully executed, returning to a previous state and executing the first action command again.

### Claim 20 (depends on 11)

The method according to claim 11, wherein the first planning path of the specific planning tree is an unscheduled plan comprising the action commands, and the step of generating the first scheduling plan according to the first planning path of the specific planning tree among the planning trees further comprises:
assigning, by a scheduler, a corresponding execution time point and an execution mode to each of the action commands in the unscheduled plan to generate the first scheduling plan,
wherein the step of sequentially issuing the action commands to the specific application further comprises:
sequentially issuing the action commands to the specific application according to the execution time point of each of the action commands.

### Claim 21

An artificial intelligence planning device, comprising:
a non-transitory storage circuit, storing a code;
a processor, coupled to the storage circuit, accessing the program code for:
obtaining a problem file and a domain file associated with a specific application run on a target server, wherein the domain file comprises N description segments corresponding to N planning layers of the specific application, N is a positive integer greater than or equal to 1, and the problem file indicates a first initial state and a target state of each of the planning layers;
constructing N planning trees corresponding to the planning layers based on the description segments, wherein each of the planning trees has a plurality of state nodes and corresponds to one of the planning layers;
determining a first planning path in the state nodes of each of the planning trees based on a deep learning model, wherein the first planning path of each of the planning trees guides the corresponding planning layer from the corresponding first initial state to the corresponding target state;
generating a first scheduling plan according to the first planning path of a specific planning tree among the planning trees, wherein the first scheduling plan comprises a plurality of action commands assigned with corresponding execution time point;
sequentially issuing the plurality of action commands to the specific application run on the target server according to the first scheduling plan, wherein the plurality of the action commands controls the target server to add a piece of program code to a program code of the specific application or remove a piece of program code from the program code of the specific application at the corresponding execution time point;
obtaining an operation observation generated by the specific application in response to a first action command among the action commands;
converting the operation observation of the specific application into an execution state;
determining a second initial state based on the execution state of the specific application in response to determining that the execution state of the specific application indicates that a new scheduling plan is required to be generated and determining a second planning path in the state nodes of each of the planning trees based on the deep learning model, wherein the second planning path of each of the planning trees guides the corresponding planning layer from the corresponding second initial state to the corresponding target state; and
generating a second scheduling plan according to the second planning path of the specific planning tree among the planning trees and sequentially issuing a plurality of other action commands to the specific application accordingly,
wherein the description segments comprise an Nth description segment corresponding to an Nth planning layer among the planning layers, the Nth description segment comprises a plurality of description sentences corresponding to the Nth planning layer, and each of the description sentences is expressed as follows:
precond_N->action_N->effect_N,
wherein precond_N is a pre-state, action_N is a specific action command, and effect_N is a result state which may be achieved by executing the specific action command in the pre-state,
wherein action_N comprises adding a node to the planning nodes of the Nth planning layer or removing a node from the planning nodes of the Nth planning layer, and effect_N is a planning node set formed by the planning nodes of the Nth planning layer after the specific action command is executed in the pre-state, the Nth planning layer corresponds to a code layer of the specific application, and action_N comprises adding the piece of program code to the specific application or removing the piece of program code from the specific application,
wherein the description segments comprise an ith description segment corresponding to the ith planning layer, the ith description segment comprises a plurality of first description sentences corresponding to the ith planning layer, and each of the first description sentences is expressed as follows:
precond_i->action_i->effect_i,
wherein precond_i is a pre-state, action_i is a specific action command, and effect_i is a result state which may be achieved by executing the specific action command in the pre-state;
wherein the first planning node corresponds to one of the first description sentences, and the processor is configured to further perform:
determining whether all of the second planning nodes satisfy the second preset conditions corresponding to the i+1th layer before executing action_i corresponding to the first planning node; and
executing action_i corresponding to the first planning node in response to determining that all of the second planning nodes satisfy the second preset conditions corresponding to the i+1th layer, otherwise, not executing action_i corresponding to the first planning node;
wherein the first preset conditions met by the first planning node comprise achieving a metric of the ith planning layer, and the method further comprises the following steps:
obtaining input data of the specific application, determining a plurality of reference patterns among a plurality of patterns corresponding to the ith planning layer in response to the input data by executing an input attention function, determining a plurality of feature values corresponding to the plurality of reference patterns by executing conscious attention function and estimating a fitness score corresponding to the ith planning layer according to the plurality of feature values; and
determining that the first planning node achieves the metric of the ith planning layer in response to determining that the fitness score corresponding to the ith planning layer is greater than a score threshold of the ith planning layer,
wherein the step of estimating the fitness score corresponding to the ith planning layer according to the input data further comprises:
executing an input attention function to find a plurality of reference patterns among a plurality of patterns corresponding to the ith planning layer in response to the input data;
executing a conscious attention function to find a plurality of feature values corresponding to the reference patterns; and
determining the fitness score corresponding to the ith planning layer based on the feature values.