FANCFIS: Fast adaptive neuro-complex fuzzy inference system

Yazdanbakhsh, Omolbanin; Dick, Scott

doi:10.1016/j.ijar.2018.10.018

Cited by 13 publications

(8 citation statements)

References 36 publications

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“…HTN (Hierarchical Task Network) planning is one of the most commonly used intelligent planning methods in the field of multiagent [12]. e basic process is to decompose complex tasks until they can be split into directly executable atomic-level tasks.…”

Section: Complex External Situationmentioning

confidence: 99%

“…e potential cooperative relationship detection is employed to detect whether there is a potential dependency or contradiction between the decision instruction of the agent and the decision instruction generated by other agents. After the potential cooperative relationship is confirmed, the situation interaction module releases its own situation information and cooperative relationship en return failure (4) else if U is primitive then (5) if (U, C) has a solution then (6) nondeterministically let π be any such solution (7) return π (8) else return failure (9) else (10) choose a nonprimitive task node u ∈ U (11) active ⟵ m ∈ M|task(m) is unifiable with t u (12) if active ≠ ∅ then (13) nondeterministically choose any m ∈ active (14) σ ⟵ an mgu for m and t u that renames all variables of m 6…”

Section: Multiagent Distributed Collaboration Frameworkmentioning

confidence: 99%

“…e reusable resource conflict coordination mechanism can be used by multiple decision-making agents simultaneously to generate repeated resource conflicts. For these resource conflicts, the tasks exhibiting higher priority are reserved and the tasks with lower priorities are coordinated (1) Procedure MA-HTN (s, T, D) (2) set s, initial state (3) set T, initial task set (4) set D, domain (5) initial operator ActionID � 0 (6) set P � the empty plan (7) T 0 ⟵ t belongs to T : no other task in T is constrained to precede t (8) loop (9) if T is empty then return P (10) nondeterministically choose any t in T 0 (11) if t is a primitive task then (12) choose a ground instance a for t with the smallest cost among the available resources (13) ActionID � ActionID + 1 (14) opmessage(a) � (ID, Resourceused(a), st(a), et(a), cost(a)) (15) sendlist ⟵ opmessage(a) (16) send opmessage(a) to the other planners (17) if receivelist ≠ ∅ : (18) ResourceID � Resourceused(a) (19) conflict actions a′ ⟵ a′ belongs to the extopmessages of receivelist : (20) has Resource(a′) � ResourceusedID and has the smallest st(a′) (21) and duration(st, et) is overlapped with duration(st′, et′)}, then (22) if st < st′ (23) delete the rest extopmessage in receivelist (24) Resourcestate(r) � i, Resourcest(r) � st, Resourceet(r) � et (25) delete T in T 0 and add a into P (26) else if st > st′ (27) delete opmessage(a) in sendlist (28) Resourcestate(r) � i′, Resourcest(r) � st′, Resourceet(r) � et′ (29) Backtrack (30) else delete T in T 0 and add a into P (31) if t is a nonprimitive task then (32) choose a method to decompose t into subtasks t 1 , t 2 , . .…”

Section: Coordination Mechanism Of Local Conflicts For the Contradicmentioning

confidence: 99%

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Multiagent Task Planning Based on Distributed Resource Scheduling under Command and Control Structure

Zhang

Gang

Song

et al. 2019

Mathematical Problems in Engineering

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For task planning of the command and control structure, the existing algorithms exhibit low efficiency and poor replanning quality under abnormal conditions. Given the requirements of the current accusation architecture, a distributed command and control structure model is built in this paper based on multiagents, which exploits the superiority of multiagents in achieving complex tasks. The concept of MultiAgent-HTN is proposed based on the framework. The original hierarchical task network planning algorithm is optimized, the multiagent collaboration framework is redefined, and the coordination mechanism of local conflict is developed. With the classical resource scheduling problem as the experimental background, the proposed algorithm compared with the classical HTN algorithm is drawn. According to the experimental results, the proposed algorithm exhibits higher quality and higher efficiency than the existing algorithm and the space anomaly is significant in the course of processing. The planning is more efficient and the time is more complicated and superior in solving the same problem, and the algorithm exhibits good convergence and adaptability. In the conclusion, it is proved that the distributed command and control structure proposed in this paper exhibits high practicability in relevant fields and can solve the problem of distributed command and control structure in a multiagent scenario.

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Section: Complex External Situationmentioning

confidence: 99%

Section: Multiagent Distributed Collaboration Frameworkmentioning

confidence: 99%

Section: Coordination Mechanism Of Local Conflicts For the Contradicmentioning

confidence: 99%

See 1 more Smart Citation

Multiagent Task Planning Based on Distributed Resource Scheduling under Command and Control Structure

Zhang

Gang

Song

et al. 2019

Mathematical Problems in Engineering

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“…A learning-embedded version with neuro-fuzzy on CFS namely Adaptive Neuro-Complex Fuzzy Inferential System (ANCFIS) was introduced in [46]. Two improvements of ANCFIS to decrease time complexity were introduced in [47][48]. Nonetheless, the CFIS [45] has limitations in the base set as well as the complex fuzzy operations.…”

Section: Introductionmentioning

confidence: 99%

A New Complex Fuzzy Inference System With Fuzzy Knowledge Graph and Extensions in Decision Making

et al. 2020

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Context and background: Complex fuzzy theory has a strong practical implication in many real-world applications. Complex Fuzzy Inference System (CFIS) is a powerful technique to overcome the challenges of uncertain, periodic data. However, a question is raised for CFIS: How can we deduce and predict the result in case there is little knowledge about data information and rule base? This is significance because many real applications do not have enough knowledge of rule base for inference so that the performance of systems may be low. Thus, it is necessary to have an approximate reasoning method to represent and derive final results. Motivation: Recently, the Mamdani Complex Fuzzy Inference System (M-CFIS) has been proposed with a specific inference mechanism according to the Mamdani type. A new improvement so-called the Mamdani Complex Fuzzy Inference System with Rule Reduction (M-CFIS-R) has been designed to utilize granular computing with complex similarity measures to reduce the rule base so as to gain better performance in decision-making problems. However in M-CFIS-R, testing data are checked by matching with each rule in the rule base, which leads to a high cost of computational time. Besides, if the testing data contain records that are not inferred by the rule base, the output cannot be generated. This happens in real commerce systems in which the rule base is small at the time of creation and needs to feed with new rules. Methodology: In order to handle those issues, this paper first time proposes the Fuzzy Knowledge Graph to represent the rule base in terms of linguistic labels and their relationships according to the rule set. An adjacent matrix of Fuzzy Knowledge Graph is generated for inference. When a record in the Testing dataset is given, it would be fuzzified and labelled. Each component in the record is checked with the Fuzzy Knowledge Graph by the inference mechanism in approximate reasoning called Fast Inference Search Algorithm. Then, we derive the label of the new record by the Max-Min operator. Besides, we also propose four extensions of

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“…The basic process is to decompose complex tasks until they can be decomposed into directly executable atomic-level tasks. After the decomposed tasks are completed, task allocation is carried out and finally a decision scheme is generated [11]. HTN algorithm has the following advantages in solving such problems.…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Task Planning Based on Distributed Resource Scheduling under Command and Control Architecture

Zhang¹,

Wang²,

Song³

2019

Preprint

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For the task planning problem of the command and control architecture, the existing algorithms have problems such as low efficiency and poor re-planning quality under abnormal conditions. Based on the requirements of the current accusation architecture, this paper constructs a distributed command and control architecture model based on multi-agents, which makes use of the superiority of multi-agents in dealing with complex tasks. The concept of MultiAgents-HTN is proposed under the framework. The original hierarchical task network planning algorithm is optimized, the multi-agent collaboration framework is redefined, and the coordination mechanism of local conflict is designed. Taking the classical resource scheduling problem as the experimental background, the comparison between the proposed algorithm and the classical HTN algorithm is carried out. The experimental results show that the proposed algorithm has higher quality and higher efficiency than the existing algorithm, and the space anomaly is heavy during processing. The planning is more efficient, and the time is more complicated and superior in dealing with the same problem, with good convergence and adaptability. The conclusion proves that the distributed command and control architecture proposed in this paper has high practicability in related fields and can solve the problem of distributed command and control architecture in multi-agent environment.

show abstract

FANCFIS: Fast adaptive neuro-complex fuzzy inference system

Cited by 13 publications

References 36 publications

Multiagent Task Planning Based on Distributed Resource Scheduling under Command and Control Structure

Multiagent Task Planning Based on Distributed Resource Scheduling under Command and Control Structure

A New Complex Fuzzy Inference System With Fuzzy Knowledge Graph and Extensions in Decision Making

Multi-Agent Task Planning Based on Distributed Resource Scheduling under Command and Control Architecture

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