CN106056215B - A kind of heterogeneous Agent energy force modeling matching process based on domain body - Google Patents
A kind of heterogeneous Agent energy force modeling matching process based on domain body Download PDFInfo
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Abstract
The invention discloses a kind of heterogeneous Agent energy force modeling matching process based on domain body, comprising the following steps: S1, according to from the connection between the class and class obtained in CDD meta-model in ability meta-model to be established, capacity-building meta-model；S2, class is added to ability meta-model, adds example to class, added attribute to example, object properties are determined according to the connection in ability meta-model between class, obtain the capability model in heterogeneous Agent cooperation field；S3, obtain heterogeneous Agent cooperation field capability model in ability and object instance, matched according to target-ability matching process, determine the matching degree of some ability and target；S4, according to the matching degree of ability and target, identify the self-ability and collaboration capabilities of Agent.The present invention energy various abilities of detailed description Agent, make system make full use of Agent ability, help to improve Agent ability cooperation efficiency, are applicable to the application platforms such as multi-Agent, multirobot, the cooperation of more AGV vehicle abilities.
Description
Technical field
The present invention relates in cooperation Adaptable System (Collaborative Self-adaptive System, CSS)
Heterogeneous Agent technical field more particularly to a kind of heterogeneous Agent energy force modeling matching process based on domain body.
Background technique
Adaptable System can perceive the variation of environment, and the change of environment is adapted to by adjusting the structure and behavior of itself
Change, it has the characteristics that open environment, variation sensibility and system dynamic.Multi-Agent Adaptable System is multiple Agent
(capableing of the execution movement of independent and flexible under a certain environment to meet the behavior entity of design object) reciprocation is constituted
System.How multi-Agent cooperates, and to reach system more effectively adaptively be current focus of attention for execution movement.Therefore, more
The research of Agent Adaptable System and its collaboration method causes the concern studied both at home and abroad.For example, COBOT :-a towards association
Research and the research of robot adaptive cooperation of the Adaptable System software frame of work etc..In Agent system (MAS)
Collaboration mode and distributed optimization technology can be applied in Adaptable System.Such cooperation Adaptable System needs to handle
The problem of between demand and software architecture.Since system runtime environment is in continuous dynamic change, Adaptable System should be wanted
Ask can context-aware, and call the Agent of different abilities to adjust itself behavior to adapt to the variation of context.
Ability is defined as to realize the ability and capacity of a target under determining field environment.
Ability represents the level of available capability, it is understood to ability, and personality, theme or a field will complete a target；
Capacity means available resource, such as time, money, personnel and tool etc..Ability (Capability) is introduced
Demand model towards Adaptable System to cooperate between agency with ability, enhances the exchange between agency, and makes every
A agency can not only complete the task within oneself ability, and appointing except oneself ability can also be completed with other proxy collaborations
Business.
The characteristics of for Adaptable System and modeling requirement, the method including REAssuRE, RELAX all use already
Concept based on Target Modeling, however these methods do not consider the demand as context and enforceability.It is based on
Goal decomposition is that sub-goal perhaps task and then analyzes which sub-goal or task have by the method for Target Modeling, such methods
The movement and behavior of a little Agent.Knowledge Modeling ontology is proposed in document, and proposes that task-capability is matched, and solves list
Adaptation sexual intercourse between a ability and individual task, but can not judge suitable between a competence set and a set of tasks
Answer sexual intercourse.For the Adaptable System that cooperates, current trend is had become using heterogeneous Agent energy force modeling.Isomorphism Agent is
Refer to structure, the identical Agent of feature and function, each Agent ability having the same；Heterogeneous Agent refer to structure, feature or
Function different Agent, each Agent have different abilities, are completed except each ability by certain cooperation mode
Thing.Demand model towards Adaptable System can solve context and enforceability based on target, task and planning
Demand, but the cooperation for being used to describe between heterogeneous Agent has some shortcomings.First, the demand model towards Adaptable System
It is only applicable between isomorphism Agent, and lacks reciprocation between Agent；Second, each Agent only complete oneself ability model
Interior task is enclosed, Agent, which cannot be cooperated, completes some task, prevent resource is from efficiently using；Third does not account for
The mutex relation of ability between mutex relation between the different abilities of Agent, and difference Agent.
Semantic description is carried out to Agent ability using the Agent ability modeling method based on domain body, can be identified
The self-ability and collaboration capabilities of Agent, can satisfy Agent changing role and multiple Agent movement interaction scenario under for
When the Dynamic Recognition and selection of Agent movement；Using Goal-Capability matching process, to Goal and Capability into
Row semantic matches judge some or the certain ability of Agent if appropriate for target according to matched degree.
Summary of the invention
The technical problem to be solved in the present invention is that for Agent changing role and multiple is unable to satisfy in the prior art
Agent is acted under interaction scenario for the Dynamic Recognition of Agent movement and the defect of selection, is provided a kind of by modeling and matching
To identify the heterogeneous Agent energy force modeling based on domain body of the collaboration capabilities between Agent self-ability and Agent
Method of completing the square.
The technical solution adopted by the present invention to solve the technical problems is:
The present invention provides a kind of heterogeneous Agent energy force modeling matching process based on domain body, comprising the following steps:
S1, from the connection between the class and class obtained in CDD meta-model in ability meta-model to be established, according to these classes
Connection capacity-building meta-model between class；
S2, the ability meta-model according to foundation, add class to ability meta-model, add example to class, add and belong to example
Property, object properties are determined according to the connection in ability meta-model between class, obtain the capability model in heterogeneous Agent cooperation field；
S3, obtain heterogeneous Agent cooperation field capability model in ability and object instance, according to target-ability
Method of completing the square is matched, and determines the matching degree of some ability and target；
S4, according to the matching degree of ability and target, identify the self-ability and collaboration capabilities of Agent.
Further, in step S1 of the invention capacity-building meta-model method specifically:
S11, from CDD meta-model extract related notion as the part class in ability meta-model；
S12, the addition new class relevant to field on the basis of metaclass；
S13, metaclass and new class collectively constitute the class in meta-model, and establish the connection between class.
Further, the method for the capability model in heterogeneous Agent cooperation field is established in step S2 of the invention are as follows:
S21, connection between corresponding class and class is added to meta-model according to specific field；
S22, according to the connection between the class in model, determine object properties；
S23, example is added to each class；
S24, attribute, including object properties are added to each example.
Further, target-ability matching process specific steps in step S3 of the invention are as follows:
S31, an ability example and an object instance are obtained from capability model；
If the element in S32, the ability and object instance binary group that obtain all is made of a context state, hold
Row step S33；Otherwise, step S34 is executed；
S33, by the element of the context state of the element in ability example binary group and object instance binary group up and down
Literary state carries out semantic matches, if exact matching, the ability and target exactly match；Otherwise, the ability and target mismatch；
S34, by the element of each context state of the element in ability example binary group and object instance binary group
Each context state carry out semantic matches, if being all exact matching, the ability and target are exactly matched；If at least one
Matching is included to semanteme, then ability includes to match with target；Otherwise, ability and target mismatch；
Further, in step S33 of the invention context state semantic matches method are as follows:
(a) predicate in two context states is compared, if semantic equivalence or identical, is executed step (b)；Otherwise
Two context states mismatch；
(b) two parameters in two context states are compared respectively, are complete if semantic equivalence or identical
Matching；If semanteme include or in capability model one belong to another subclass, for include matching；Otherwise for not
Match.
Further, the method for the self-ability and collaboration capabilities of Agent is identified in step S4 of the invention specifically:
If matching result is exact matching, the ability that indicates is the self-ability of Agent；If include matching, then it represents that should
Ability is the collaboration capabilities of Agent, is otherwise non-Agent ability.
The beneficial effect comprise that: the heterogeneous Agent energy force modeling match party of the invention based on domain body
Method, by the way that Agent ability is modeled and is matched, to identify the collaboration capabilities between Agent self-ability and Agent,
To determine which component can complete target under current state, this method energy various abilities of detailed description Agent make system
Agent ability is made full use of, Agent ability cooperation efficiency is helped to improve, is applicable to multi-Agent, multirobot, more
The application platforms such as AGV vehicle ability cooperation.
Detailed description of the invention
Present invention will be further explained below with reference to the attached drawings and examples, in attached drawing:
Fig. 1 is the flow chart of the heterogeneous Agent energy force modeling matching process based on domain body of the embodiment of the present invention；
Fig. 2 is the CDD meta-model of the heterogeneous Agent energy force modeling matching process based on domain body of the embodiment of the present invention
Figure；
Fig. 3 is the ability member mould of the heterogeneous Agent energy force modeling matching process based on domain body of the embodiment of the present invention
Type figure；
Fig. 4 is the embodiment of the heterogeneous Agent energy force modeling matching process based on domain body of the embodiment of the present invention
Process flow diagram flow chart.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that described herein, specific examples are only used to explain the present invention, not
For limiting the present invention.
As shown in Figure 1, the heterogeneous Agent energy force modeling matching process based on domain body, comprising the following steps:
S1, it is obtained in built meta-model from existing CDD (Capability Driven Development) meta-model
Class, the connection between class, capacity-building meta-model；
S11, from CDD meta-model extract related notion as the part class in ability meta-model；
S12, the addition new class relevant to field on the basis of metaclass；
S13, metaclass and new class collectively constitute the class in meta-model, and establish the connection between class.
S2, the ability meta-model according to foundation, establish the capability model in heterogeneous Agent cooperation field, including give meta-model
Addition class determines object properties to class addition example, to example addition attribute, according to the connection in meta-model between class；
S21, connection between corresponding class and class is added to meta-model according to specific field；
S22, according to the connection between the class in model, determine object properties；
S23, example is added to each class；
S24, attribute (including object properties) are added to each example.
S3, obtain heterogeneous Agent cooperation field capability model in ability and object instance, according to target-ability
Method of completing the square is matched, and determines the matching degree of some ability and target；
S31, an ability example and an object instance are obtained from capability model；
If the element in S32, the ability and object instance binary group that obtain all is made of a context state, hold
Row step S33；Otherwise, step S34 is executed；
S33, by the element of the context state of the element in ability example binary group and object instance binary group up and down
Literary state carries out semantic matches, if exact matching, the ability and target exactly match；Otherwise, the ability and target mismatch；
S34, by the element of each context state of the element in ability example binary group and object instance binary group
Each context state carry out semantic matches, if being all exact matching, the ability and target are exactly matched；If at least one
Matching is included to semanteme, then ability includes to match with target；Otherwise, ability and target mismatch；
S4, according to the matching degree of ability and target, identify the self-ability and collaboration capabilities of Agent.
If matching result is exact matching, the ability that indicates is the self-ability of Agent；If include matching, then it represents that should
Ability is the collaboration capabilities of Agent, is otherwise non-Agent ability.
In another specific embodiment of the invention, steps of the method are:
Step 1: from acquisition institute in existing CDD (Capability Driven Development) meta-model (see Fig. 2)
The class in meta-model, the connection between class are built, capacity-building meta-model (see Fig. 3):
(1a) is from related notion is extracted as the part class in ability meta-model in CDD meta-model；
In embodiment, tri- concepts of Capability, Goal and ContextSet are extracted from CDD meta-model as ability
A part of meta-model.
(1b) addition new class relevant to field on the basis of metaclass；
In embodiment, Goal is divided to for two kinds of Agent Goal and Social Goal, wherein Goal by binary group G=<
TriggerCondition, FinalState > expression, TriggerCondition indicate to trigger item required for completing target
Part, FinalState indicate end-state achieved after the completion of target；Capability by binary group C=<
Precondition, Effect > expression, Precondition indicate the precondition of ability, and Effect indicates the effect of ability
Effect.TriggerCondition, FinalState, Precondition, Effect are by context state
ContextState=<argument1, predicate, argument2>expression；
(1c) metaclass and new class collectively constitute the class in meta-model, and establish the connection between class；
Step 2: establish the capability model in heterogeneous Agent cooperation field:
(2a) adds the connection between corresponding class and class to meta-model according to specific field, mainly has in embodiment
Tri- major class of Capability, Goal and ContextState.Capability is made of Precondition and Effect,
Goal is made of TriggerCondition and FinalState, they are all indicated by ContextState；
There are two main classes of Capability and ContextSet in embodiment, Capability and ContextSet are
It is to be obtained by the actual process of AGV transporting medical rubbish.Actual process is as shown in Figure 3.In addition, each field
It can be related to many field concepts, next modeling work, can build a DomainConcept class, be used to for convenience
Store concept relevant to the field；
(2b) determines object properties according to the connection between the class in model；
In embodiment, the attribute of all ContextSet examples and ability is expressed as " argument1_
Predicate_argument2 " form, in semantic predicate logical expressions, they are expressed as " predicate
(argument1, argument2) "；According to the relationship between each element in Fig. 1, we are available " agent_has_
Capability ", " AGV_has_state ", " argument1 ", " argument2 ", " hasInConstraints ",
" hasOutConstraints ", " propertyPredicate " these object properties, wherein " hasInConstraints "
Indicate that capability has Precondition attribute, " hasOutConstraints " indicates that capability has Effect category
Property.
(2c) adds example to each class；
To Capability, ContextSet, DomainConcept, Predicate class adds example；
In embodiment, Capability has 14, and ContextSet has 25, and predicate has 14.
(2d): attribute is added to the example in (2c)；
In embodiment, " hasInConstraints " mainly is added to Capability, " hasOutConstraints "
Attribute, adds " argument1 " to ContextSet, and " argument2 " and " predicate " attribute is added to Agent
" agent_has_capability " attribute adds " AGV_has_state " attribute to AGV.
Step 3: the Goal in the Capability and embodiment in model is pressed into Goal-Capability matching process
Match:
(3a) will be in the ContextState of the element T riggerCondition, FinalState of Capability
Predicate respectively in the ContextState of the element Precondition, Effect of Goal predicate carry out
Match, if exact matching, executes step (3b)；If not exact matching, then Capability and Goal is mismatched；
(3b) will be in the ContextState of the element T riggerCondition, FinalState of Capability
Argument1 respectively in the ContextState of the element Precondition, Effect of Goal argument1 carry out
Match, and by argument2 in the ContextState of the element T riggerCondition, FinalState of Capability
It is matched respectively with the argument2 in the ContextState of the element Precondition, Effect of Goal, if being all
Exact matching, then Capability and target exact matching；If all for comprising matching, Capability and Goal include
Match；
In embodiment, " DetectCart " ability of Capability is expressed as: C1:DetectCart
InContraints={ in (cart, cart_pickup) }
OutConstraints={ ready (cart_position) }
And target G1 " Get cart ' s position " is indicated are as follows: Get cart ' s position=< cart_
Sensor, { in (cart, cart_pickup) }, { ready (pickup_position) } >, according to Capability-Goal
Method of completing the square matches " the Trigger Condition " of " InContraints " of ability C1 and G1, C1's
" OutContraints " is matched with " the Final State " of G1.If some ability or target include multiple
ContextState is then needed the ContextState progress in each ContextState and Goal in Capability
Match, the matching degree of Capability and Goal are determined with this.
Step 4: the self-ability and collaboration capabilities of Agent are identified according to matching result: if in Capability set
Each Capability has corresponding Goal to exactly match therewith in Goal set, then Capability is Agent
Self-ability；If it includes that the Goal in Capability and Goal set in Capability set, which at least has a pair to be,
Match, then Capability is the collaboration capabilities of Agent.
According to the matching in 3, if matching result is exact matching, the ability that indicates is the self-ability of Agent；If comprising
Matching, then it represents that the ability is the collaboration capabilities of Agent, is otherwise non-Agent ability.
It should be understood that for those of ordinary skills, it can be modified or changed according to the above description,
And all these modifications and variations should all belong to the protection domain of appended claims of the present invention.
Claims (1)
1. a kind of heterogeneous Agent energy force modeling matching process based on domain body, which comprises the following steps:
S1, from the connection between the class and class obtained in CDD meta-model in ability meta-model to be established, according to these classes and class
Between connection capacity-building meta-model；
S2, the ability meta-model according to foundation, add class to ability meta-model, add example to class, add attribute, root to example
Object properties are determined according to the connection between class in ability meta-model, obtain the capability model in heterogeneous Agent cooperation field；
S3, obtain heterogeneous Agent cooperation field capability model in ability and object instance, according to target-ability match party
Method is matched, and determines the matching degree of some ability and target；
S4, according to the matching degree of ability and target, identify the self-ability and collaboration capabilities of Agent；
The method of capacity-building meta-model in step S1 specifically:
S11, from CDD meta-model extract related notion as the part class in ability meta-model；
S12, the addition new class relevant to field on the basis of metaclass；
S13, metaclass and new class collectively constitute the class in meta-model, and establish the connection between class；
The method of the capability model in heterogeneous Agent cooperation field is established in step S2 are as follows:
S21, connection between corresponding class and class is added to meta-model according to specific field；
S22, according to the connection between the class in model, determine object properties；
S23, example is added to each class；
S24, attribute, including object properties are added to each example；
Target-ability matching process specific steps in step S3 are as follows:
S31, an ability example and an object instance are obtained from capability model；
If the element in S32, the ability and object instance binary group that obtain all is made of a context state, step is executed
Rapid S33；Otherwise, step S34 is executed；
S33, by the context shape of the context state of the element in ability example binary group and the element of object instance binary group
State carries out semantic matches, if exact matching, the ability and target exactly match；Otherwise, the ability and target mismatch；
S34, by the every of each context state of the element in ability example binary group and the element of object instance binary group
One context state carries out semantic matches, if being all exact matching, the ability and target are exactly matched；If at least a pair of of language
Justice includes matching, then ability includes to match with target；Otherwise, ability and target mismatch；
The method of context state semantic matches in step S33 are as follows:
(a) predicate in two context states is compared, if semantic equivalence or identical, is executed step (b)；Otherwise two
Context state mismatches；
(b) two parameters in two context states are compared respectively, are complete if semantic equivalence or identical
Match；If semanteme include or in capability model one belong to another subclass, for include matching；It otherwise is mismatch；
The method of the self-ability and collaboration capabilities of Agent is identified in step S4 specifically:
If matching result is exact matching, the ability that indicates is the self-ability of Agent；If include matching, then it represents that the ability
It is otherwise non-Agent ability for the collaboration capabilities of Agent.
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| CN102831318A (en) * | 2012-08-25 | 2012-12-19 | 北京科技大学 | Task allocation algorithm based on individual capacity in heterogeneous multi-robot system |
| CN103150623A (en) * | 2012-12-27 | 2013-06-12 | 北京仿真中心 | Cloud manufacture capacity service modeling method based on meta-model |
| CN104951898A (en) * | 2015-07-02 | 2015-09-30 | 北京理工大学 | Task-oriented cooperative multi-agent coalition formation method |
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| CN102831318A (en) * | 2012-08-25 | 2012-12-19 | 北京科技大学 | Task allocation algorithm based on individual capacity in heterogeneous multi-robot system |
| CN103150623A (en) * | 2012-12-27 | 2013-06-12 | 北京仿真中心 | Cloud manufacture capacity service modeling method based on meta-model |
| CN104951898A (en) * | 2015-07-02 | 2015-09-30 | 北京理工大学 | Task-oriented cooperative multi-agent coalition formation method |
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