CN108958030B - Artificial intelligence combat method and robot system based on knowledge base - Google Patents
Artificial intelligence combat method and robot system based on knowledge base Download PDFInfo
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Abstract
The artificial intelligence battle method and robot system based on knowledge base includes: generating a plurality of first combat case samples; generating the operation situation in the first operation case sample according to a preset operation situation knowledge base; generating the fighting intention in the first fighting case sample according to a preset fighting intention knowledge base; generating a combat decision in the first combat case sample according to a preset combat decision knowledge base; and screening a plurality of first combat case samples meeting preset conditions from the plurality of first combat case samples to serve as a plurality of second combat case samples. The method and the system solve the problem that few combat case samples can not be used for effective aid decision-making through automatic generation of the combat case samples, improve the capability of aid decision-making in combat and improve the subjective activity and intelligence of the combat robot.
Description
Technical Field
The invention relates to the technical field of information, in particular to an artificial intelligence combat method and a robot system based on a knowledge base.
Background
The knowledge base is one of important technologies in artificial intelligence, and can assist human in making decisions.
The syndrome differentiation is divided into a stage of differentiation of thinking, a stage of excess syndrome, and a unified stage of differentiation of thinking and excess syndrome. The excessive stage is to examine the results of the thinking stage. The stage of unity of thinking and excess is actually the stage of practicing the results of thinking stage through examination and screening of excess stage.
The commonly used teaching stages in the teaching method comprise a self-learning stage, a teaching stage and an examination stage.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art: the existing combat robot only installs a program written in advance into the robot, does not take the robot as a person to educate, and also lacks dialectical philosophy, so that the robot lacks subjective initiative and objective dialectical of similar people in actual wars. The existing combat robots based on artificial intelligence all need a large number of case learning to realize intelligence, but actually, the number of samples of combat cases is seriously insufficient, because peaceful period actual combat and combat exercises are few, and the combat demonstration cost is high, a large number of combat case samples cannot be obtained, but a large number of artificial intelligence algorithms can realize effective effects only depending on a large number of case samples, so that the combat robots are difficult to play the roles and advantages of the artificial intelligence.
Accordingly, the prior art is yet to be improved and developed.
Disclosure of Invention
Based on this, it is necessary to provide an artificial intelligence combat method and a robot system based on a knowledge base for overcoming the defects or shortcomings of the combat technique in the prior art, such as insufficient combat case samples and limited artificial intelligence application in the prior art.
In a first aspect, an embodiment of the present invention provides a combat method, where the method includes:
a sample generation step: generating a plurality of first combat case samples;
and (3) sample screening: and screening a plurality of first combat case samples meeting preset conditions from the plurality of first combat case samples to serve as a plurality of second combat case samples.
Preferably, the combat case samples comprise combat situations, combat intentions and combat decisions.
Preferably, the sample generating step specifically includes:
and (3) situation generation: generating the operation situation in the first operation case sample according to a preset operation situation knowledge base;
an intention generation step: generating the fighting intention in the first fighting case sample according to a preset fighting intention knowledge base;
a decision generation step: generating a combat decision in the first combat case sample according to a preset combat decision knowledge base;
the sample screening step specifically comprises:
a combat simulation step: simulating and executing the operation decision in the first operation case sample under the operation situation in the first operation case sample to obtain an operation result;
and a result matching step: calculating the matching degree of the combat result and the combat intention in the first combat case sample;
matching judgment: judging whether the matching degree is greater than a preset threshold value: and if so, taking the operation situation, the operation intention and the operation decision as a plurality of second operation case samples.
Preferably, the method further comprises, after the step of screening the sample:
a sample verification step: screening out a plurality of real operation case samples of which the matching degree of the operation results and the operation intentions is greater than a preset threshold value, and verifying a plurality of second operation case samples;
a verification judgment step: judging whether the verification passes: if so, taking the plurality of second combat case samples and the plurality of real combat case samples as a plurality of third combat case samples; otherwise, the step of generating the sample is carried out continuously.
Preferably, after the step of verifying and judging, the method further comprises:
a decision generation step: matching the third operation case samples according to operation situation and operation intention to obtain operation decision;
a sample optimization step: after the combat decision generates a combat result, the combat result is obtained, the matching degree of the combat result and the combat intention is calculated, and whether the matching degree is greater than a preset threshold value is judged: otherwise, returning to the step of generating the sample and continuing to execute.
In a second aspect, an embodiment of the present invention provides a combat system, including:
a sample generation module: generating a plurality of first combat case samples; the combat case sample comprises a combat situation, a combat intention and a combat decision;
a sample screening module: and screening a plurality of first combat case samples meeting preset conditions from the plurality of first combat case samples to serve as a plurality of second combat case samples.
Preferably, the first and second electrodes are formed of a metal,
the sample generation module specifically comprises:
a situation generation module: generating the operation situation in the first operation case sample according to a preset operation situation knowledge base;
an intent generation module: generating the fighting intention in the first fighting case sample according to a preset fighting intention knowledge base;
a decision generation module: generating a combat decision in the first combat case sample according to a preset combat decision knowledge base;
the sample screening module specifically comprises:
the combat simulation module: simulating and executing the operation decision in the first operation case sample under the operation situation in the first operation case sample to obtain an operation result;
a result matching module: calculating the matching degree of the combat result and the combat intention in the first combat case sample;
a matching judgment module: judging whether the matching degree is greater than a preset threshold value: and if so, taking the operation situation, the operation intention and the operation decision as a plurality of second operation case samples.
Preferably, the system further comprises:
a sample verification module: screening out a plurality of real operation case samples of which the matching degree of the operation results and the operation intentions is greater than a preset threshold value, and verifying a plurality of second operation case samples;
a verification judgment module: judging whether the verification passes: if so, taking the plurality of second combat case samples and the plurality of real combat case samples as a plurality of third combat case samples; otherwise, the method goes to the sample generation module to continue execution.
Preferably, the system further comprises:
a decision generation module: matching the third operation case samples according to operation situation and operation intention to obtain operation decision;
a sample optimization module: after the combat decision generates a combat result, the combat result is obtained, the matching degree of the combat result and the combat intention is calculated, and whether the matching degree is greater than a preset threshold value is judged: otherwise, returning to the step of generating the sample and continuing to execute.
In a third aspect, an embodiment of the present invention provides a robot system, in which the combat systems according to the second aspect are respectively arranged.
The embodiment of the invention has the following beneficial effects:
a knowledge base-based artificial intelligence combat method and a robot system comprise: generating a plurality of first combat case samples; generating the operation situation in the first operation case sample according to a preset operation situation knowledge base; generating the fighting intention in the first fighting case sample according to a preset fighting intention knowledge base; generating a combat decision in the first combat case sample according to a preset combat decision knowledge base; and screening a plurality of first combat case samples meeting preset conditions from the plurality of first combat case samples to serve as a plurality of second combat case samples. The robot is used as a human to educate, so that the robot has human-like subjective activity in actual wars. A plurality of combat case samples are generated by (self-learning phase, thinking phase) construction (preferably, the combat case samples are stored in a combat case knowledge base), then tested and refined by (teaching phase, practice phase) real combat cases, and finally verified and further refined by (examination phase, unification phase) practice or exercise. The whole process is the same as the teaching process, and is also the process of dialectical development, and the combined boxing has the effect which cannot be achieved by singly using a knowledge base at a certain stage, so that a plurality of required combat case samples do not only depend on the samples of real combat cases, but can be generated under the condition of no real samples, and then the test and the improvement are carried out on the basis of the samples of the real combat cases. The method and the system solve the problem that effective auxiliary decision-making cannot be carried out due to few combat case samples through automatic generation of the combat case samples, improve the capacity of the combat auxiliary decision-making, and improve the subjective activity and the intelligence of the combat robot.
Drawings
Fig. 1 is a flowchart of a combat method provided in embodiment 1 of the present invention;
FIG. 2 is a flowchart of a sample generation step of the battle method provided in embodiment 2 of the present invention;
FIG. 3 is a flowchart of a sample screening step of the battle method according to embodiment 2 of the present invention;
fig. 4 is a flowchart of a combat method according to embodiment 3 of the present invention;
FIG. 5 is a flowchart of a combat method according to embodiment 4 of the present invention;
fig. 6 is a functional block diagram of a combat system provided in embodiment 5 of the present invention;
FIG. 7 is a schematic block diagram of a sample generation module of the combat system provided in embodiment 6 of the present invention;
FIG. 8 is a schematic block diagram of a sample screening module of the combat system provided in embodiment 6 of the present invention;
fig. 9 is a functional block diagram of a combat system provided in embodiment 7 of the present invention;
fig. 10 is a schematic block diagram of a combat system according to embodiment 8 of the present invention.
Detailed Description
The technical solutions in the examples of the present invention are described in detail below with reference to the embodiments of the present invention.
Embodiment 1 provides a battle method, as shown in fig. 1, including steps S110 to S120.
Step S110 to step S120: the combat case knowledge base is generated through self-learning. In the thinking and distinguishing stage of the operation, the robot can understand the operation situation and the operation intention and distinguish the thinking and distinguishing of the operation decision. This stage corresponds to the self-learning stage of the teaching method, since this stage is mainly the robot self-learning to generate the battle case samples (which can be stored in the battle case knowledge base).
Sample generation step S110: and generating a plurality of first combat case samples, wherein the first combat case samples comprise combat situations, combat intentions and combat decisions of a preset party. Preferably, a plurality of the first case samples form first case sample big data. Adding a plurality of the first case samples to a first case knowledge base. Specifically, the situation, the intent and the decision of the first combat case are used as the specific data of three fields of each case in the combat case knowledge base, and the data table in the combat case knowledge base comprises three fields, namely a situation field, an intent field and a decision field.
Sample screening step S120: and screening a plurality of first combat case samples meeting preset conditions from the plurality of first combat case samples to serve as a plurality of second combat case samples. Preferably, the preset conditions include that the operation situation in the first operation case sample and the operation result under the operation decision are consistent with the preset party operation intention in the first operation case sample. Preferably, the second case sample is added to a second case knowledge base. Preferably, a plurality of first case samples are obtained from the first case knowledge base. It can be understood that, since the amount of the first combat case sample is very large, the amount of the second combat case sample screened from the first combat case sample is also very large, a large number of second combat case samples can be formed, and when the number is large, large data of the second combat case sample can be formed.
The method has the advantages that the operation case sample data of the operation situation, the operation intention and the operation decision of the preset party are automatically generated through the knowledge base instead of sampling from the actual operation case, so that the problems that in peace period, the actual war is few, the operation exercise cost is high, and the operation case samples are very few and are not enough to form a large number of operation case samples are solved.
Example 2 a preferred method of combat is provided, according to the method of combat described in example 1,
as shown in fig. 2, the process specifically generated in the sample generation step S110 includes:
situation generating step S111: and generating the operation situation in the first operation case sample according to a preset operation situation knowledge base. Preferably, the combat situation knowledge base is constructed in advance, and the combat situation knowledge base stores the combat situation composition rule sub-knowledge base and the combat situation composition element sub-knowledge base in advance. The combat situation composition rule sub-knowledge base comprises combination rules of enemy attributes, enemy capabilities, real-time states of the enemies, attributes of the own parties, capabilities of the own parties and real-time states of the own parties. The sub-knowledge base of the elements forming the battle situation comprises an attribute knowledge table, a capability knowledge table, a real-time state knowledge table and the like, wherein the attribute knowledge table, the capability knowledge table, the real-time state knowledge table and the like are knowledge tables of the elements forming the relevant battle situation. The specific process of S111 is to randomly acquire an instance of the attribute from the attribute knowledge table as an enemy attribute, randomly acquire an instance of the capability from the capability knowledge table as an enemy capability, randomly acquire an instance of the real-time status from the real-time status knowledge table as an enemy real-time status, similarly acquire a property of my, a capability of my, and a real-time status of my from the related battle situation configuration element knowledge table, and then combine them to form a battle situation including the attribute of enemy, the capability of enemy, the real-time status of enemy, the attribute of my, the capability of my, and the real-time status of my.
Intent generation step S112: and generating the fighting intention in the first fighting case sample according to a preset fighting intention knowledge base. Preferably, the operation intention knowledge base is constructed in advance, and the operation intention construction rule sub knowledge base and the operation intention construction element sub knowledge base are stored in advance in the operation intention knowledge base. The fighting intention composition rule sub-knowledge base comprises combined rules of the fighting intention of the my party and the fighting intention of the enemy. The operation intention constituent element sub-knowledge base comprises an operation intention constituent element knowledge table. Specific combat intentions constitute elements such as attack wins, lossless retreat, tracking scout enemy directions, and the like. The specific process of S112 is to randomly acquire one example of the fighting intention from the fighting intention constituent element knowledge table as the enemy fighting intention or set the enemy intention as unknown with a certain probability, randomly acquire one example of the fighting intention from the fighting intention constituent element knowledge table as the my fighting intention, and then combine to form the fighting intention including the my fighting intention and the enemy fighting intention. In this case, the enemy engagement intention may be set to be unknown at random, because the enemy intention is to be obtained by intelligence or reconnaissance in actual wars and sometimes the enemy intention is not necessarily known, so the enemy intention may be set to be unknown.
Decision generation step S113: and generating the operation decision in the first operation case sample according to a preset operation decision knowledge base. Preferably, the operation decision knowledge base is pre-constructed, and the operation decision formation rule sub-knowledge base and the operation decision formation element sub-knowledge base are pre-stored in the operation decision knowledge base. The operation decision forming rule sub-knowledge base comprises combination rules including the type of operation, the time of operation, the place of operation and the target of operation. The sub-knowledge base of elements for the operation decision comprises an operation type knowledge table, an operation time knowledge table, an operation place knowledge table and an operation target knowledge table. The type of the operation is, for example, missile launching, the operation time is the time for missile launching, the operation place is the place for missile launching, and the operation target is the attack target for missile launching. The specific process of S113 is to randomly acquire an instance of the operation type from the operation type knowledge table as the operation type, and an instance of the operation time from the operation time knowledge table as the operation time, and similarly acquire an operation place and an operation target, and then combine them to form an operation decision of the preset party. The predetermined party operational decision here generally refers to the operational decision of the party of my or friend.
As shown in fig. 3, the specific process of the sample screening step S120 is:
battle simulation step S121: and simulating and executing the operation decision in the first operation case sample under the operation situation in the first operation case sample to obtain an operation result. Preferably, the simulation of the execution of the combat decision under the combat situation can be realized by existing combat simulation software, and the simulation result is obtained, for example, the combat situation is that an opponent fighter is 30 meters ahead of the own fighter, the own fighter is an X-type fighter, the opponent fighter is a Y-type fighter, the combat decision is that the own fighter emits a cannonball, and the combat result is that the opponent fighter is hit down. The fighting situation includes, for example, attributes of the enemy and my fighters (e.g., aircraft model, missile model), performance (e.g., flight speed, shooting accuracy and shooting speed of missile), and flight trajectory (e.g., real-time trajectory of the enemy and my fighters). The combat decision launches a missile, for example, at time t1, to an enemy plane.
Result matching step S122: and calculating the matching degree of the operation result and the operation intention of a preset party in the first operation case sample. For example, if the result of the battle is that my fighter hits the opponent fighter, the match will succeed if my's operational intention is also to hit the opponent fighter. The preset party generally refers to my party or friend party.
Matching judgment step S123: judging whether the matching degree is greater than a preset threshold value: if so, the operation decision in the first operation case sample is effective to the operation situation in the first operation case sample and the operation intention in the first operation case sample, and the first operation case sample is used as a second operation case sample and added into a second operation case knowledge base; and if not, the operation decision in the first operation case sample is invalid for the operation situation in the first operation case sample and the operation intention in the first operation case sample.
The embodiment has the advantages that the system automatically generates the combat case samples, various samples can be generated by combination even if no or few actual samples exist, and more case samples than actual combat can be generated as long as the combination times are enough, so that a larger number of combat case samples than actual combat cases can be formed.
Embodiment 3 provides a preferable battle method according to the battle method of embodiment 1 or embodiment 2, as shown in fig. 4, further comprising step S210 and step S220 after the step S120:
steps S210 to S220 belong to a battle practice stage (essentially, a stage of verifying a plurality of the battle case samples generated). This phase corresponds at the same time to the teaching phase of the teaching method, since this phase is essentially the testing and improvement of the battle case samples generated in the self-teaching phase by the real battle cases.
Sample verification step S210: and screening out a plurality of real operation case samples of which the matching degree of the operation results and the operation intentions is greater than a preset threshold value, and verifying the plurality of second operation case samples. The real combat case samples comprise combat situations, combat intentions, combat decisions and combat results. The understandable real combat case sample is a sample of the actually occurred combat case, and therefore, the actual combat case sample is necessarily the sample with the combat result. Specifically, a plurality of real operation case samples are obtained, real operation case samples with the matching degree of operation results and operation intentions larger than a preset threshold value are selected from the real operation case samples, operation situation, operation intentions and operation decisions of a preset party in the selected real operation cases are extracted, the operation situation and the operation intentions are matched with the operation situation and the operation intentions in each second operation case sample in the plurality of second operation case samples, the second operation case sample with the maximum matching degree is obtained, and the operation decisions in the second operation case sample with the maximum matching degree are extracted. Matching the operation decision in the second operation case sample with the maximum matching degree with the operation decision of a preset party in the selected real operation case, and judging whether the matching degree is greater than a preset threshold value: if yes, the test is successful; otherwise, the test fails. Preferably, after the step of failing the test, the second battle case sample with the largest matching degree is deleted from the plurality of second battle case samples. The real combat case samples comprise combat case samples in actual combat or in practice.
Verification judgment step S220: judging whether the verification passes: if so, taking the plurality of second combat case samples and the plurality of real combat case samples as a plurality of third combat case samples; otherwise, go to the sample generation step S110 and continue execution. Preferably, the plurality of third case samples are added to a third case knowledge base. The specific process comprises the following steps: the test in the specific process in the sample verification step S210 is performed multiple times, and it is determined whether the number of times of successful test is greater than or equal to a preset ratio to the total number of times of test: if yes, the verification is passed; otherwise, the verification is not passed, and the process returns to the sample generation step S110 to continue the execution.
The advantage of this embodiment is that the second case sample generated is determined by testing whether it can pass the test of the real case, because the real case actually occurred in the past, if the test is passed, it is described that the second case sample can pass the practical test, the third case sample. If the test is successful, the case for test is further used as a third operation case sample, and the third operation case sample is added into the operation case knowledge base to enrich the operation case sample.
Embodiment 4 provides a preferable battle method according to embodiment 3, as shown in fig. 5, further comprising step S310 and step S320 after the step S220.
The steps S310 to S320 belong to a unified stage of battle, which is essentially a stage of using the third battle case sample. This phase corresponds to the examination phase of the teaching method, since this phase is primarily the application and further examination of the battle case samples generated in the two phases of self-teaching and teaching.
Decision generation step S310: and matching the plurality of third combat case samples according to the combat situation and the combat intention of a preset party to obtain a combat decision, wherein the combat decision is used as the combat decision of the preset party. Preferably, the operation situation and the operation intention of the preset party are obtained, the operation situation and the operation intention are matched with the operation situation and the operation intention in each third operation case sample in a plurality of third operation case samples (preferably, the third operation case samples are obtained from a third operation case knowledge base), the third operation case sample with the maximum matching degree is obtained, the operation decision in the third operation case sample with the maximum matching degree is extracted, and the extracted operation decision is recommended to an operator of the preset party (for example, a fighter sending a driving fighter by voice) or an operation instruction (for example, a missile launching instruction) capable of controlling the operation equipment related to the operation decision can be directly converted through the internet of things, so that the behavior of the operation equipment is directly controlled through the internet of things. The obtained fighting situation and the fighting intention comprise the fighting situation and the fighting intention during the fighting exercise or during the actual fighting.
Sample optimization step S320: preferably, after the combat decision generates a combat result, obtaining the combat result corresponding to the combat decision, calculating a matching degree between the combat result and the combat intention of the preset party, and judging whether the matching degree is greater than a preset threshold: if so, the operation decision is effective to the operation situation and the operation intention, and the operation situation, the operation intention and the operation decision are associated and then added to be used as a new third operation case sample (or added to a third operation case knowledge base); otherwise, the operation decision is invalid for the operation situation and the operation intention, and the operation decision returns to the sample generation step S110 to continue execution. It is understood that if the combat decision fails, it indicates that the plurality of third combat case samples need to be further improved, so that the return to the sample generation step S110 needs to be performed again.
The benefit of this embodiment is, inspects third operation case sample through exercise and actual combat for along with the development of exercise or actual combat, the third operation sample can be added to the effectual operation case constantly, makes third operation case sample can be more and more abundant. Meanwhile, if the result is invalid, the third battle case sample is re-optimized.
The systems in embodiment 5, embodiment 6, embodiment 7, and embodiment 8 correspond to and are similar to the methods in embodiment 1, embodiment 2, embodiment 3, and embodiment 4, respectively, so the preferred implementation and beneficial effects are not described again, and only the main modules thereof are given.
Embodiment 5 provides a battle system, as shown in fig. 6, which includes modules 110 to 120.
The sample generation module 110: and generating a plurality of first combat case samples, wherein the first combat case samples comprise combat situations, combat intentions and combat decisions of a preset party.
The sample screening module 120: and screening a plurality of first combat case samples meeting preset conditions from the plurality of first combat case samples to serve as a plurality of second combat case samples.
Example 6 a preferred combat system is provided, according to the combat system described in example 5,
as shown in fig. 7, the specific generation process in the sample generation module 110 includes:
the situation generation module 111: and generating the operation situation in the first operation case sample according to a preset operation situation knowledge base.
The intent generation module 112: and generating the fighting intention in the first fighting case sample according to a preset fighting intention knowledge base.
The decision generation module 113: and generating the operation decision in the first operation case sample according to a preset operation decision knowledge base.
As shown in fig. 8, the specific process of the sample screening module 120 includes:
the combat simulation module 121: and simulating and executing the operation decision in the first operation case sample under the operation situation in the first operation case sample to obtain an operation result.
The result matching module 122: and calculating the matching degree of the operation result and the operation intention of a preset party in the first operation case sample.
The matching judgment module S123: judging whether the matching degree is greater than a preset threshold value: if so, the operation decision in the first operation case sample is effective to the operation situation in the first operation case sample and the operation intention in the first operation case sample, and the first operation case sample is used as a second operation case sample and added into a second operation case knowledge base; and if not, the operation decision in the first operation case sample is invalid for the operation situation in the first operation case sample and the operation intention in the first operation case sample.
Embodiment 7 provides a preferable battle system according to embodiment 5 or embodiment 6, as shown in fig. 9, further comprising a module 210 and a module 220 after the module 120:
the sample validation module 210: and screening out a plurality of real operation case samples of which the matching degree of the operation results and the operation intentions is greater than a preset threshold value, and verifying the plurality of second operation case samples.
The verification judgment module 220: judging whether the verification passes: if so, taking the plurality of second combat case samples and the plurality of real combat case samples as a plurality of third combat case samples; otherwise, go to the sample generation module 110 to continue execution.
Embodiment 8 provides a preferable battle system according to embodiment 7, further comprising a module 310 and a module 320 after the module 220, as shown in fig. 10.
The decision generation module 310: and matching the plurality of third combat case samples according to the combat situation and the combat intention of a preset party to obtain a combat decision, wherein the combat decision is used as the combat decision of the preset party.
The sample optimization module 320: preferably, after the combat decision generates a combat result, obtaining the combat result corresponding to the combat decision, calculating a matching degree between the combat result and the combat intention of the preset party, and judging whether the matching degree is greater than a preset threshold: if so, the operation decision is effective to the operation situation and the operation intention, and the operation situation, the operation intention and the operation decision are associated and then added into an operation case knowledge base; if not, the fighting decision is invalid for the fighting situation and the fighting intention, and the sample generation module 110 is returned to continue execution.
Embodiment 9 provides a robot system in which the combat systems according to any one of embodiments 5 to 8 are respectively arranged.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for those skilled in the art, various changes and modifications can be made without departing from the spirit of the present invention, and these changes and modifications are within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (9)
1. A method of combat, the method comprising:
a sample generation step: generating a plurality of first combat case samples;
and (3) sample screening: screening a plurality of first combat case samples meeting preset conditions from the plurality of first combat case samples to serve as a plurality of second combat case samples;
the combat case sample comprises a combat situation, a combat intention and a combat decision;
the sample generating step specifically comprises:
and (3) situation generation: generating the operation situation in the first operation case sample according to a preset operation situation knowledge base; the combat situation knowledge base is pre-stored with a combat situation composition rule sub-knowledge base and a combat situation composition element sub-knowledge base; the combat situation composition rule sub-knowledge base comprises combination rules of enemy attributes, enemy capabilities, real-time states of the enemies, attributes of the own parties, capabilities of the own parties and real-time states of the own parties; the combat situation constituent element sub-knowledge base comprises an attribute knowledge table, a capability knowledge table and a knowledge table of relevant combat situation constituent elements of a real-time state knowledge table;
an intention generation step: generating the fighting intention in the first fighting case sample according to a preset fighting intention knowledge base; the fighting intention knowledge base is pre-stored with a fighting intention forming rule sub-knowledge base and a fighting intention forming element sub-knowledge base; the fighting intention forming rule sub-knowledge base comprises a combination rule of the fighting intention of the party and the fighting intention of the enemy; the combat intention constituent element sub-knowledge base comprises a combat intention constituent element knowledge table;
a decision generation step: generating a combat decision in the first combat case sample according to a preset combat decision knowledge base; the combat decision knowledge base is pre-stored with a combat decision forming rule sub-knowledge base and a combat decision forming element sub-knowledge base; the operation decision forming rule sub-knowledge base comprises combination rules of operation types, operation time, operation places and operation targets; the sub-knowledge base of elements for the operation decision comprises an operation type knowledge table, an operation time knowledge table, an operation place knowledge table and an operation target knowledge table.
2. The combat method of claim 1 wherein,
the sample screening step specifically comprises:
a combat simulation step: simulating and executing the operation decision in the first operation case sample under the operation situation in the first operation case sample to obtain an operation result;
and a result matching step: calculating the matching degree of the combat result and the combat intention in the first combat case sample;
matching judgment: judging whether the matching degree is greater than a preset threshold value: and if so, taking the operation situation, the operation intention and the operation decision as a plurality of second operation case samples.
3. The method of combat according to claim 1 or 2, further comprising, after said step of screening said samples:
a sample verification step: screening out a plurality of real operation case samples of which the matching degree of the operation results and the operation intentions is greater than a preset threshold value, and verifying a plurality of second operation case samples;
a verification judgment step: judging whether the verification passes: if so, taking the plurality of second combat case samples and the plurality of real combat case samples as a plurality of third combat case samples; otherwise, the step of generating the sample is carried out continuously.
4. The combat method of claim 3, further comprising, after said step of validating and determining:
a decision generation step: matching the third operation case samples according to operation situation and operation intention to obtain operation decision;
a sample optimization step: after the combat decision generates a combat result, the combat result is obtained, the matching degree of the combat result and the combat intention is calculated, and whether the matching degree is greater than a preset threshold value is judged: otherwise, returning to the step of generating the sample and continuing to execute.
5. A combat system, the system comprising:
a sample generation module: generating a plurality of first combat case samples; the combat case sample comprises a combat situation, a combat intention and a combat decision;
a sample screening module: screening a plurality of first combat case samples meeting preset conditions from the plurality of first combat case samples to serve as a plurality of second combat case samples;
the combat situation knowledge base is pre-stored with a combat situation composition rule sub-knowledge base and a combat situation composition element sub-knowledge base; the combat situation composition rule sub-knowledge base comprises combination rules of enemy attributes, enemy capabilities, real-time states of the enemies, attributes of the own parties, capabilities of the own parties and real-time states of the own parties; the combat situation constituent element sub-knowledge base comprises an attribute knowledge table, a capability knowledge table and a knowledge table of relevant combat situation constituent elements of a real-time state knowledge table; the fighting intention knowledge base is pre-stored with a fighting intention forming rule sub-knowledge base and a fighting intention forming element sub-knowledge base; the fighting intention forming rule sub-knowledge base comprises a combination rule of the fighting intention of the party and the fighting intention of the enemy; the combat intention constituent element sub-knowledge base comprises a combat intention constituent element knowledge table; the combat decision knowledge base is pre-stored with a combat decision forming rule sub-knowledge base and a combat decision forming element sub-knowledge base; the operation decision forming rule sub-knowledge base comprises combination rules of operation types, operation time, operation places and operation targets; the sub-knowledge base of elements for the operation decision comprises an operation type knowledge table, an operation time knowledge table, an operation place knowledge table and an operation target knowledge table.
6. The combat system of claim 5,
the sample generation module specifically comprises:
a situation generation module: generating the operation situation in the first operation case sample according to a preset operation situation knowledge base;
an intent generation module: generating the fighting intention in the first fighting case sample according to a preset fighting intention knowledge base;
a decision generation module: generating a combat decision in the first combat case sample according to a preset combat decision knowledge base;
the sample screening module specifically comprises:
the combat simulation module: simulating and executing the operation decision in the first operation case sample under the operation situation in the first operation case sample to obtain an operation result;
a result matching module: calculating the matching degree of the combat result and the combat intention in the first combat case sample;
a matching judgment module: judging whether the matching degree is greater than a preset threshold value: and if so, taking the operation situation, the operation intention and the operation decision as a plurality of second operation case samples.
7. The combat system of claim 5 or 6, wherein the system further comprises:
a sample verification module: screening out a plurality of real operation case samples of which the matching degree of the operation results and the operation intentions is greater than a preset threshold value, and verifying a plurality of second operation case samples;
a verification judgment module: judging whether the verification passes: if so, taking the plurality of second combat case samples and the plurality of real combat case samples as a plurality of third combat case samples; otherwise, the method goes to the sample generation module to continue execution.
8. The warfare system of claim 7, further comprising:
a decision generation module: matching the third operation case samples according to operation situation and operation intention to obtain operation decision;
a sample optimization module: after the combat decision generates a combat result, the combat result is obtained, the matching degree of the combat result and the combat intention is calculated, and whether the matching degree is greater than a preset threshold value is judged: otherwise, returning to the step of generating the sample and continuing to execute.
9. A robot system characterized in that the fighter systems according to any one of claims 5 to 8 are respectively arranged in the robots.
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