CN112766505A - Knowledge representation method of non-monotonic reasoning in logic action language system depiction - Google Patents
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Abstract
The invention discloses a knowledge representation method of non-monotonic reasoning in logic action language system depiction. The method carries out abstract depiction and property representation on a plurality of classical logic action languages, and provides the minimum feature set of the languages under the uniform characteristic space. The method comprises three contents of systematic automatic translation, deterministic semantic characteristic constraint set construction, stable model semantic mapping and consistency verification of a logic action language under the syntax of an answer set logic program. Logical action language the present invention can represent and analyze logical action language systematically, and improve the applicability and efficiency of knowledge representation and inference for different logical action language systems.
Description
Technical Field
The invention relates to the technical field of information processing, in particular to a knowledge representation method of non-monotonic reasoning in logic action language system depiction.
Background
Non-monotonic reasoning is an important theory and method in the logic-based artificial intelligence field, which not only solves the branch Problem (Ramification Problem) in the knowledge representation, but also the idea is applied to the design and implementation of various logic action languages, and various logic action language systems are generated. These logical action languages are characterized by their use in different scenarios and tasks. However, many logical action languages have complex mapping relationships and lack systematic analysis or comparison tools. For example, the logic action language B, C and the causal theory of the forest truth, solve the branch problem when no causal closed loop exists in the theorem system, and can also translate with each other. However, when a causal closed loop occurs in the system, the three have different performances and the relationship between the three is not clear. There is a need for a system to analyze logical action languages to study the characteristics of different semantics.
The rapid development of inferential solution systems has facilitated the generation of a wide variety of logical action languages. In recent years, a variety of logical action languages have been proposed for different presentation and reasoning needs. For example, B, C +, BC +, LPMLN, and the like are widely noted and recognized. These logical action languages (methods) have their own application scenarios and characteristics, but the connection, distinction, application scope and semantic characteristics between various logical action languages (methods) lack systematic evaluation methods and analysis tools. Especially, when a Causal dependency loop (cause loop) exists in the logic system, how the logic system operates is not clear, and it is difficult to have intuitive and quantitative interpretation of different expressions of various existing logic action languages.
In the prior art, for the research of the relationship between two methods, a mutual translation mode is usually adopted, namely, the conditions and the ways of mutual translation between two languages are discussed on the premise of meeting the applicability. Or find the equivalence space of the two languages, i.e. on which scopes the semantics of the two languages are equivalent, such as the operation of Michael Gelfond and vladmirirllifschitz 2012 about the common kernel of logical action languages B and C. For the application domain, however, among a variety of different logical action languages, it is important to reasonably select a language tool suitable for an application scenario and a target task. Not only needs systematic and clear cognition on the characteristics of various logic action languages, but also needs a clear and comprehensive evaluation and analysis system and standard to provide a systematic theoretical basis for the selection of the logic action languages.
Disclosure of Invention
The present invention is directed to overcoming the above-mentioned deficiencies of the prior art and providing a method for representing knowledge in logical action language system delineation by non-monotonic reasoning.
According to a first aspect of the invention, a method of knowledge representation in logical action language system characterization for non-monotonic reasoning is provided. The method comprises the following steps:
performing systematic automatic translation on the logic action language under the answer set logic program syntax to obtain a candidate grammar translation pool;
aiming at the characteristics of the logic action language, constructing a unified semantic characteristic coordinate and a deterministic semantic characteristic constraint set under the coordinate, so that the set constraint only contains unique grammar translation from the logic action language to a logic program;
and calculating the stable model semantics of the logic program according to the unique deterministic syntax translation, systematically performing automatic semantic mapping, and verifying the consistency of the obtained model and the semantic model of the logic action language.
According to a second aspect of the invention, a framework for knowledge representation of non-monotonic reasoning in a logical action language system depiction is provided. The framework comprises an information abstraction level management module, a domain knowledge base, a logic action language representation module and a model solver, wherein: the domain knowledge base is used for storing rules, theorems and facts of a high abstraction level; the information abstraction level management module is used for classifying the management information according to abstraction levels; after the domain knowledge is extracted from the domain knowledge base by the information abstraction level management module, the domain knowledge is input into a logic action language representation module for formalization to form a theorem in a logic program; the neural network trains and learns corresponding state facts according to data of a specific application scene, and the state facts are input into the logic action language representation module through the information abstraction level management module and are formalized into facts in the logic program. And the model solver performs model calculation on the learned complete logic program to obtain a determination solution of the subsequent state.
Compared with the prior art, the method has the advantages that the characteristic depiction is carried out on the classical logic action languages which are widely concerned and accepted in the field, and the minimum characteristic depiction set is established for the logic action languages on the premise of not depending on a specific logic semantic system or a specific tool. A general semantic characteristic pool is constructed for the logic action language facing to the application scene, and a uniform characteristic coordinate is constructed for different logic action languages in similar scenes, so that a theoretical basis is provided for the design, selection and use of the logic action language.
Other features of the present invention and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a diagram illustrating the correspondence between the research content and the key questions of the present invention;
FIG. 2 is a schematic diagram of a framework for implementing the techniques of the present invention;
FIG. 3 is a systematic analysis, verification and comparison of logical action language features according to one embodiment of the present invention.
Detailed Description
Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
With reference to fig. 1 and 2, the present invention is explored for theoretical analysis and application of non-monotonic reasoning and logical action language, and mainly solves the following three technical problems:
1) unified systematic representation of logical action languages
Existing logical action language logic systems are algorithmically dependent on answer set semantics and have their obvious limitations, e.g., there are some logic systems that do not have a uniform mapping to the answer set semantic form. For these logical systems or logical action languages, system analysis is currently not possible. Based on the existing work, the invention constructs a logic action language analysis system with universality and wide trial range from the semantic model of the logic action language, and has effective analysis capability for some logic action languages which can not be mapped to the semantic logic of an answer set. The system architecture designed by the invention can establish an analysis system independent of specific logic tools.
2) Characterization and analysis of systematic semantic properties of logical action languages
The concept of finite field theorem discovery and verification in the existing logic action language description analysis system is theoretically suitable for most formal logic action languages, but only supports a plurality of classical logic action languages due to excessive dependence on a stable semantic model and a defined grammar framework which is relatively fixed. The present invention removes the dependency on specific logic tools and removes some strong assumptions embedded in the existing system, thereby creating a more versatile and flexible logic action linguistic analysis system. It is necessary to establish a grammar representation framework covering most logic action languages, and on the basis of the grammar representation framework, the existing logic action languages are analyzed and classified based on the grammar, and respective formalized mapping is given. In addition, the invention also establishes related semantic characteristics under most application scenes from the semantic perspective to serve as a semantic characteristic pool.
3) Systematic analysis, verification and comparison of logical action linguistic characteristics
The mapping relation between the existing logic action languages is complex, and an analysis or comparison tool of a system is lacked. The usual approach is to translate two specific logical action languages directly into each other or to examine their semantic properties on a typical example of a finite field. For example, the logic action language B, C and the causal theory of the forest truth, solve the branch problem when no causal closed loop exists in the theorem system, and can also translate with each other. However, when a causal closed loop occurs in the system, the three have different performances and the relationship between the three is not clear. There is a need for a system to analyze logical action languages to study the characteristics of different semantics.
The invention aims to establish a universal logic action language description analysis system and researches semantic characteristics of a plurality of current logic action languages. Referring to three levels in corresponding research content, the research scheme is discussed in three modules, including systematic automatic translation of logic action language under the syntax of an answer set logic program, construction of a deterministic semantic property constraint set, semantic mapping of a stable model and general verification.
First, systematic automatic translation under logical program grammar of answer set
For several classical logic action languages such as B, C, C +, BC, BC + and the like which are widely concerned and recognized in the current non-monotonic logic field, the invention provides a systematic analysis and verification method except individual case comparison and interconversion. In general, the systematic automatic translation process under the syntax of the answer set logic program includes: establishing a logic action language grammar data set, establishing a semantic characteristic data set, designing a logic action language grammar general mapping interface and the like.
Specifically, the classical logical action language characterization process includes:
step S1, a logical action language grammar data set is created.
The logical action language characterization analysis system has the characteristics that different logical action languages can be characterized uniformly and in a consistent form, and the mapping relation between a pair of logical action languages is not only researched. This requires the creation of a grammar data set that covers most logical action languages.
Step S2, establishing semantic characteristic data set
Different logic action languages have different semantic characteristics, and a characteristic data set covering important semantic characteristics of various classical logic action languages is established for realizing multi-language system analysis.
Step S3, designing logic action language grammar general mapping interface
The above process is further explained below by taking logic action language B as an example.
For a rule set described in B language, i.e. (S, D), which is an S static rule set and D is a dynamic rule set, the rule set is first mapped to another syntax structure except B, taking a logic program as an example:
xi in the above formula is a uniform grammar translation interface, and a logic action language composed of static rules and dynamic rules is formally translated into a logic program corresponding to static and dynamic grammar translation interfaces xiSAnd xiD. In the rule set T ═ (S, D), S is a static rule set, D is a static rule set, and ξ (T) is a logic program output after passing through the unified syntax translation interface. The output program is divided into three parts, the first part
The second part
Sub-logic programs corresponding to rules in static and dynamic rule sets respectively, and the third part of Base is a basic axiom set, such as propositionThe uniqueness is true. Based on the mapping-to-rule decomposable assumption, for a rule set T under the description of a logic action language, the mapped theorem system is composed of sub-theorem systems corresponding to each rule in T under the mapping. To remove the extraneous factors, the mapping of the individual rules obeys the fact-dependent principle, i.e., the fact not included in the mapped rule is not introduced into the mapped theorem.
In particular, for a static rule (l, G), ξ under a B language descriptionSAll rules contained in (l, G) are of the form:
l′←F′1,not F′2 (2)
where l 'is a successor of l, F'1And F'2Is a conjunctive of any successor fact, but does not contain the form f' and satisfies
Successor facts of (1).
The third part Base is a basic theoretic assumption, including the law of inertia, that is, for any fact f, the following theorem holds:
where not is the default as Failure and f' is the true value of f in the subsequent state. An allowable mapping called T is when this mapping satisfies two conditions: 1) the mapping is resolvable based on rules; 2) the mapping may rotate for homogeneous structures. In a limited name space N, all possible rule sets formed by static and dynamic rules described by all types of B languages are exhausted, all logic programs obtained under the possible allowable mapping are calculated, and all model solutions under the stable model semantics are calculated. For each model solution, a minimum feature set is used to verify its satisfaction with each feature in the set. If there is exactly one and only one stable model corresponding to the allowable mapping for each possible rule set, and all the characteristics in the minimum characteristic set are satisfied, the minimum characteristic set is a theoretical characteristic depiction of the B language on the namespace N. The mapping of the logic program in the above example may be replaced with any other syntax mapping. Therefore, in the system framework design, the universal mapping interface is not limited to a certain logic tool, such as an answer set language model.
Second, deterministic semantic property constraint set construction
The process analyzes and compares the universal logic action language and verifies the characteristics of the new language. Specifically, on the basis of systematic automatic translation under the logic program grammar of the answer set, the invention establishes a general characteristic pool for logic action languages and application scenes in general forms, and finds a minimum characteristic description set for the action languages under the principle of uniqueness mapping, and specifically comprises the following steps:
and step S4, determining a finite field.
And determining a finite field as an experimental basis in a system description theoretical framework. The selection requirement of the finite field is appropriate, the requirements of popularization verification and certification are met, and the calculation possibility is considered.
Step S5, finding characteristic combination
Feature combinations are found to constrain the computation of the semantic model.
Step S6, depicting uniqueness verification on finite field
And verifying the selected feature combination on the determined finite field to determine whether to provide the unique semantic depiction for the target logical action language, and returning to the step S5 if the verification fails until the minimum semantic feature set is found to uniquely depict the semantics of the target logical action language.
The semantic property pool of the logic action language covers important properties of the logic action language under most application scenes, and comprises the following steps:
(1) isomorphism to Classical Negative Exchange (CNE): for any given set of rules T described in the logical action language, and any given fact f, if(s)1,a,s2) Is a model of T, then
Is also TfA model of (2). Wherein
A is a legal action. For state s and fact f, sfThe definition is as follows:
and TfIs all of f in T and
the resulting rule sets are interchanged.
(2) State constraint Satisfaction (SAT): for a rule set under any given logical action language description, T ═ (S, D), the state constraints corresponding to all the rules in its static rule set S must be satisfied by each model of T.
(3) Negative loops of singleton can be deleted (SNR): for any given rule set under the description of the logic action language, T ═ S, D, if the following form of rule (Head, Cond) is contained in the static rule set S and satisfied
This rule can be deleted directly without any model change to T.
(4) Static and dynamic rules model no branching (DRF) when isolatable: for any given rule set T under the logical action language description, if there is no rule (Head, Cond) in the dynamic rule set D that satisfies the following condition:
then in the successor state there is no change to the true value except for the fact that the dynamic rule directly results in a change. I.e.(s)1,a,s2) A model that is T is satisfied if and only if:
s1＝s2∪s+\s-
and s1And s2And satisfying the state constraints corresponding to all the static rules in the static rule set S. Wherein s is+Is the fact that action a is true in the direct result, where s-Is the fact that action a is false in the direct result.
(5) Static, dynamic rules model no branching (DARF) when isolatable: for any given rule set T under the logical action language description, if there is no rule (Head, Cond) in the dynamic rule set D that satisfies the following condition:
and in the logic rule, the causal relationship directed graph does not contain any simple loop (namely, causal relationship closed loop), so that in the subsequent state, no other change is caused except the fact true value change directly caused by the dynamic rule. I.e.(s)1,a,s2) A model that is T is satisfied if and only if:
s1＝s2∪s+\s-
s+∩s-＝Φ
and s1And s2And satisfying the state constraints corresponding to all the static rules in the S. Wherein s is+Is the fact that action a is true in the direct result, where s-Is the fact that action a is false in the direct result.
(6) Supporting prerequisite equivalence merging (EC): for a rule set under a given logical action language description, T ═ (S, D), in a static rule S, if there are two rules of a common header fact, e.g.
(l,G1)
(l,G2)
Then both rules support a merge operation in similar classical logic:
(l,G1∪G2)
in the existing logical action language, C partially satisfies this characteristic, while B does not.
Thirdly, semantic mapping and consistency verification of stable model
On the basis of the unique deterministic semantic constraint set, the module calculates the semantics of a logic program stable model, maps the semantics back to the semantic space of the logic action language, and performs consistency verification on the original semantic model of the action language, wherein the verification result is shown in figure 3. Specifically, for the classical logic action languages B, C and BC, under the unified semantic property coordinate system, the respective minimum semantic property constraint sets are subjected to consistency verification through stable model semantic mapping, and different combinations on the property space uniquely describe the semantics of B, C, BC.
The above logical action language characteristics have different importance in different application fields, and for different purposes of use, the selection or design of the logical action language should consider the combination of different characteristics. In fact, there are many other characteristics to semantic models for various languages. The invention arranges, discovers and proves the relationship of inclusion, independence, orthogonality and the like among the characteristics, and after a complete and comprehensive logic action language characteristic pool is established, various logic action languages can be depicted, analyzed and compared under the same characteristic coordinate, thereby providing theoretical basis for the selection or design of different applications on the logic action languages.
In conclusion, the method establishes the characteristic portraits for various classical logic action languages, and compared with the current analysis system based on the answer set semantic model, the method does not depend on a specific logic tool and has higher universality and tolerance; compared with the prior art based on analysis, comparison and inter-translation of certain two logic action languages, the invention applies a non-monotonic reasoning method to construct a universal logic action language system, provides a uniform characteristic depiction space, and multiple languages simultaneously depict, analyze, compare and evaluate on multiple characteristic indexes such as scope, inertia embeddability, causal closed loop support and the like, thereby providing a theoretical basis for the design, selection and use of the logic action language. In addition, the invention starts from the embedding of a non-monotonic reasoning module, researches the theoretical possibility of the combination of a logic-based artificial intelligence method and a deep learning method, provides information classification based on a knowledge level to improve the learning efficiency, and introduces reasoning facing to high-abstraction level information to enable the logic method to be further applied in a deep learning framework. Furthermore, the invention combines non-monotonic reasoning with deep reinforcement learning, is applied to the field of game artificial intelligence application, and expands the application scene of logic-based artificial intelligence related theories and methods.
The present invention may be a system, method and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied therewith for causing a processor to implement various aspects of the present invention.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present invention may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present invention are implemented by personalizing an electronic circuit, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA), with state information of computer-readable program instructions, which can execute the computer-readable program instructions.
Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, by software, and by a combination of software and hardware are equivalent.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.
Claims (10)
1. A method of knowledge representation in logical action language system characterization for non-monotonic reasoning comprising the steps of:
performing systematic automatic translation on the logic action language under the answer set logic program syntax to obtain a candidate grammar translation pool;
aiming at the characteristics of the logic action language, constructing a unified semantic characteristic coordinate and a deterministic semantic characteristic constraint set under the coordinate, so that only a unique deterministic syntax translation from the logic action language to a logic program is contained under the constraint of the set;
and calculating the stable model semantics of the logic program according to the unique deterministic syntax translation, systematically performing automatic semantic mapping, and verifying the consistency of the obtained model and the semantic model of the logic action language.
2. The method of claim 1, wherein the deterministic set of semantic property constraints is constructed according to the following steps:
establishing a logic action language grammar data set;
constructing a universal semantic characteristic data set facing an application scene, and constructing uniform characteristic coordinates for different logic action languages in similar scenes;
determining a finite field as an experimental basis for logic action language portrayal;
finding combinations of features to constrain the computation of the semantic model;
and verifying the selected characteristic combination on the determined finite field to determine whether to provide a unique semantic depiction for the target logic action language, and further determining a minimum characteristic depiction set corresponding to the target logic action language.
3. The method of claim 1, wherein the generic semantic property coordinates are constructed according to the following principles: isomorphism to the classical negative exchange; satisfying a state constraint; false rings of singletons can be deleted; no branch exists when static and dynamic rules are isolated; static and dynamic rule isolation and no branch when no cause and effect loop exists; and supporting precondition equivalence merging.
4. The method of claim 3, wherein the isomorphism for a classical negative swap is represented as:
for a given rule set T under the description of a logical action language, and any given fact f, if(s)1,a,s2) Is a model of T, then
Is also TfA model of wherein
For world states, a is a legal action, for state s and facts f, sfThe definition is as follows:
and TfIs all of f in T and
the resulting rule sets are interchanged.
5. The method of claim 3, wherein the satisfying a state constraint is represented as:
for a rule set under a given logical action language description, T ═ (S, D), the state constraints corresponding to all the rules in its static rule set S must be satisfied by each model of T.
6. The method of claim 3, wherein the single-fact negation ring deletable is represented as:
for a rule set under a given logical action language description, T ═ (S, D), if a rule (Head, Cond) is contained in the static rule set S and satisfied
The rule can be deleted directly without any model change to T.
7. The method of claim 3, wherein the static and dynamic rule isolation time-invariant branchless is represented as:
for a given rule set T under the logical action language description, if there is no rule (Head, Cond) in the dynamic rule set D that satisfies the following condition:
head ∈ C, then in the successor state, there is no change other than the true value change directly caused by the dynamic rule, i.e.(s)1,a,s2) A model that is T is satisfied if and only if:
s1＝s2∪s+\s-
s+∩s-＝Φ
s1and s2Satisfy in SAnd state constraints corresponding to all static rules.
8. The method of claim 3, wherein the static, dynamic rule-isolated and causality-free loop-time-free branch is represented as:
for a given rule set T under the logical action language description, if there is no rule (Head, Cond) in the dynamic rule set D that satisfies the following condition:
head ∈ C, and there is no closed causal dependency loop in the rule-causal directed graph, then in the successor state there is no change to the truth value other than the change in the fact that the dynamic rule directly causes, i.e.(s)1,a,s2) A model that is T is satisfied if and only if:
s1＝s2∪s+\s-
s+∩s-＝Φ
s1and s2And satisfying the state constraints corresponding to all the static rules in the S.
9. The method of claim 3, wherein the supporting premise equivalence combining is expressed as:
for a rule set under a given logical action language description, T ═ S, D, if there are two rules of a common header fact in a static rule S:
(l,G1)
(l,G2)
the two rules support a merge operation:
(l,G1∪G2)。
10. a computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 9.
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