Multiagent Collaborative Computation

The growing use of autonomous tractor fleets with detachable implements presents complex logistical challenges in agriculture. Current systems often rely on simple heuristics and avoid implement swapping, limiting efficiency. A central challenge is to dynamically coordinate vehicle routing and implement exchanges to enable efficient, low-intervention task execution. Due to high costs, such fleets are owned mainly by large enterprises or cooperatives, where fair task allocation and profit sharing are critical. Addressing both coordination and fairness, in this paper, we introduce the Agricultural Fleet Vehicle Routing Problem with Implements (AFVRPI). We propose a distributed model derived from a centralized formulation also presented in this paper. This model is embedded within a Distributed Multi-Agent System Architecture (DIMASA), where autonomous vehicle agents manage routing and implement use under limited fuel autonomy, while implement agents ensure compatibility and sufficient capacity to meet task demands. Our solution applies systematic egalitarian social welfare optimization to iteratively maximize the profit of the worst-off vehicle, balancing fairness with system efficiency. To enhance scalability, we use column generation in the distributed model, achieving solution quality comparable to the centralized model while significantly reducing computing time. Simulation results on new benchmark instances demonstrate that our distributed multi-agent AFVRPI approach is scalable, efficient, and fair.

Genetic Regulatory System (GRS) and Deoxyribonucleic Acid (DNA) are two important matters in the development and functioning of organisms. GRS is a mechanism for determining when, where, in what form and to which extent a gene or genes will be expressed, while DNA is the essential part of a gene. In this paper we address the utilization of Multiagent Collaborative Computation (MCC) to model the genes' interactions in GRS. The aim of the modeling is to examine each gene contribution to the interactions in a series of times manner. By having the knowledge of their interactions, we can infer their behavior and make estimations in order to obtain better interactions' products in the future.

Knowledge Growing System (KGS) is a novel perspective in Artificial Intelligence (AI) which is aimed to emulate how the human brain obtains new knowledge from information delivered by human sensory organs. The new knowledge is then used as the basis for making estimation in the future of the phenomenon being observed as the basis for the most appropriate decision or action that will be decided or taken. In this paper we address the application of KGS to infer the behavior of genes interaction in Genetic Regulatory System (GRS) in order to estimate their behavior in the subsequent interaction time. For this purpose we model the genes as multi-agent that performs collaborative computations in Multiagent Collaborative Computation (MCC) paradigm. The knowledge regarding the genes behavior is obtained by applying a novel information-inferencing fusion method called Observation Multi-time Arwin-Adang-Aciek-Sembiring (OMA3S). In order to show how KGS works in MCC framework, we use yeast genes-interaction values as the case study

—Computation occurred within human brain is very much awesome and is not possible to be emulated 100% exactly in Artificial Intelligence (AI) method-based machines. What scientists did and have been done so far up to now are to try to model it as close as to what exactly occurs within the brain. Human brain has an awesome mechanism in performing computation with the end result is new knowledge and human uses the knowledge to actuate his organs. In this paper we will show a new approach for emulating the computation occured within human brain to obtain new knowledge based on the inputs sensed by the system's sensory system taken from the environment. When this process is carried out recursively, the system's knowledge becomes newer and newer, and it is called as knowledge growing. This approach is designed for an agent that has ability to think and act rationally like human. Our cognitive modelling approach is resulted in a model of human information processing and a technique to obtain the most maximum performance should be taken by the cognitive agent. This method is called as A3S (Arwin-Adang-Aciek-Sembiring), the agent is called as Knowledge-Growing System (KGS) and this brain-inspired method opens a new perspective in AI that we call as Cognitive Artificial Intelligence (CAI).

This paper is a report on our research progress in the area of aircraft  identification by utilizing neural networks and information fusion. In this paper we address the performance comparison of supervised and unsupervised neural networks in aircraft identification tasks in a generic system called Neural Network-based Aircraft Identification System (NN-AIS). We select Adaptive Resonance Theory (ART) for the unsupervised neural network and Back Propagation Network (BPN) for the supervised one. As for previous research, we use two kinds of input namely aircraft Radar Cross Section (RCS) and average speed. Their performance will be validated by using already-learnt and never-learnt patterns

Knowledge-Growing System (KGS) is a novel perspective in Artificial Intelligent, which is aimed to emulate how human thinks in the view of how human brain grows its knowledge as the time passes and uses it to apprehend the phenomenon being observed. In order to obtain the knowledge, the brain receives a set of information from the human sensory organs regarding a phenomenon, fuses the information to obtain an inference, and this inference then becomes new knowledge. This mechanism will cycle until the brain apprehends the observed phenomenon. in this paper, a preliminary study to design a Cognitive Processor based on KGS algorithm will be presented. The processor is planned to be implemented in System on Chip (SoC). By implementing KGS algorithm to a processor, it will be possible to have a hardware system which emulates the intelligence shown by the human brain. This paper will discuss the steps in constructing the architecture of a Cognitive Processor based on KGS algorithm

Making a strategic decision is one of important matters especially in a military organization such as Indonesian Air Force (IDAF). There are many cases which need an accurate and quick decision in the IDAF such as selecting the most representative weaponry systems to replace the old ones. In this paper we propose the use of A3S information-inferencing fusion method for selecting a new light transport aircraft from four prospective aircraft candidates. The result of the selection will be used by the decision maker to select the best light transport aircraft candidate as the new weaponry system. In this paper we use original data from four aircraft candidates. After computed by using A3S method, we found that aircraft “A” is the most probable candidate to replace the old aircraft “X” with Degree of Certainty (DoC) 27,8%.

Knowledge growing is one of intelligence characteristics possessed by human brain. In this paper we review some theories that are appropriate for emulating this kind of intelligence in order to develop an intelligent system in Artificial Intelligence (AI) field, called brain-inspired
Knowledge-Growing System (KGS). Based on the review results, we have built a design concept of this system along with its mechanism for growing the knowledge called Observation Multitime Arwin-Adang-Aciek-Sembiring (OMA3S) information-inferencing fusion method

Brain-inspired Knowledge-Growing System (KGS) is a system that is capable of growing its knowledge along with the accretion of information it receives as the time passes. In its core KGS is an intelligent agent that is equipped with knowledge-growing mechanism. This mechanism is the emulation of human thought process, in the sense how the brain grows the knowledge from the information perceived by its sensory organs. In order to obtain the most appropriate emulation of the knowledgegrowing mechanism, we have to know in advance how this mechanism occurs in human brain. In this paper we deliver our survey to apprehending this mechanism from psychological perspective. After studying some psychological models, we build our own knowledge-growing mechanism for KGS called Sense-Inference and Decision Formulation-Decide and Act (SIDA) cycle that will be
implemented to our KGS

Knowledge Growing System (KGS) is a novel perspective in Artificial Intelligence (AI) which is aimed to emulate how the human brain obtains new knowledge from information delivered by human sensory organs. The new knowledge is then used as the basis for making an estimation in the future of the phenomenon being observed as the basis for the
most appropriate decision or action that will be decided or taken.
In this paper we address the application of KGS to infer the behavior of genes interaction in Genetic Regulatory System (GRS) in order to estimate their behavior in the subsequent interaction time. For this purpose we model the genes as multi-agent that performs collaborative computations in Multiagent Collaborative Computation (MCC) paradigm. In order to show how KGS works in MCC framework, we use yeast genes-interaction values as the case study.

Genetic Regulatory System (GRS) and Deoxy- ribonucleic Acid (DNA) are two important matters in the development and functioning of organisms. GRS is a mechanism for determining when, where, in what form and to which extent a gene or genes will be expressed, while DNA is the essential part of a gene. In this paper we address the utilization of Multiagent Collaborative Computation (MCC) to model the genes’ interactions in GRS. The aim of the modeling is to examine each gene contribution to the interactions in a series of times manner. By having the knowledge of the genes interactions, we can infer their behavior and make estimations in order to obtain better interactions’ products in the future

Quick-and-accurate decision is a primary requirement for achieving a mission’s successfulness in many organizations whether they are militaries or civilians. The larger and more complex the scope of a
mission the longer the time will be needed to perform decision-making. A complex mission such as retaining a sustainable mission of aircrafts deployed in a certain remote base will require a 24-hour of standby on-site or off-site maintenance teams. The inherent problem arisen
from humans is the fatigue that can occur anytime to any of them during a continuing mission and of course, it can affect the continuity of the mission and delay or disappoint the mission achievement. In this paper we address the concept of the utilization of Multiagent Collaborative Computation (MCC) paradigm in supporting the decision-making process in aircrafts maintenance during a sustainable mission in a remote area.

This paper is a report of our research progress in the area of pattern recognition in endeavouring finding a novel method for aircraft identification. In this paper we address the performance comparison between intelligent and unintelligent approaches in performing aircraft identification tasks in a generic system called Generic Aircraft Identification System (GAIS) especially in accessing the knowledge stored in database. For this purpose, we select two types of neural networks namely, Back Propagation Network (BPN) for the supervised exemplar and Adaptive Resonance Theory (ART) for the unsupervised one for intelligent identification approach. For unintelligent approach we select standard approach in database technology namely linked list. As for previous research, we use two kinds of input namely aircraft Radar Cross Section (RCS) and average speed. Their performance will be validated by using already learnt and never-learnt patterns.

In this paper we address an approach on how to emulate human’s capability in obtaining comprehensive inferencing after observing a phenomenon in its environment. Before making a decision or taking an action regarding a situation that is occurring or probably occurs in the future, human observes the situation dynamics by using his/her
sensing organs and communicating with other people.

Information collected by the sensing organs is delivered to the brain to be fused to obtain inferencing. Comprehensive inferencing will be obtained after the previous inferencing is fused with new inferencing obtained from other sources. This human’s capability will bring a benefit to humankind if it can be emulated and applied to intelligent agents. For
this purpose, we propose a new information-inferencing fusion method called A3S (Arwin-Adang-Aciek-Sembiring) as an emulation of human information-inferencing fusion mechanism.

Artificial Intelligence (AI) is a field that has a primary objective of creating systems that carry human-intelligence characteristics. The methods and techniques in AI can be approached from symbolic and computational processing perspectives. The computational approaches are called as Computational Intelligence (CI). In this paper we address the creation of systems that emulate the way of human thinks. The
advantage of this approach is the systems can adapt the environment changing after perceiving the information delivered from their sensory organs. The perceived information is intelligently processed by human brain to obtain new knowledge. By having new knowledge they can adapt the environment dynamics faster and more accurate. Based on this reason we deliver a system that is able to grow its knowledge along with the accretion of information it receives as the time passes which is
called as Knowledge Growing System (KGS). The knowledge-growing
mechanism is mimicked from the mechanism occurred in human inference system when creating new knowledge by taking into consideration uncertainties that may occur. To obtain this mechanism so as to be applied to KGS, we have developed a probability-computation-based method called Observation Multi-Time A3S (OMA3S) information-inferencing fusion. The experiment results show that our proposed method brings a new perspective in AI with a new computational method viewed from CI

Knowledge-Growing System (KGS) is a system that is capable of growing its knowledge along with the accretion of information as the time passes. It is aimed to mimic the way of human thinks that is, the way of his brain grows the knowledge regarding a phenomenon after receiving information from his sensory organs relating to it. A way to have an understanding how this system works is by emulating it in a computing-based system; and it can only be done by designing its
mathematical model at first. In this paper we present the step-by-step mathematical
modeling of KGS based on our observation on human inference system. There will be five steps in obtaining new knowledge that is information fusion, information-inferencing, information-inferencing fusion, knowledge-inferencing, and knowledge-inferencing fusion. This 5-step process is performed by our new method called Observation Multi-time A3S (OMA3S), a mathematical model of knowledge-growing process. Because of its capability in learning a phenomenon by growing the
knowledge relating to it, KGS is declared as a novel perspective in Artificial Intelligence (AI).

Military Operation (MO) is a very complex problem where many parties are involved to accomplish the planned mission. In a sustain mission that needs a full attention and may be running for a long period, there are
possibilities that the commanding officer and his supporting staffs as well as the field commanders become exhausted and this situation can affect the mission’s objective accomplishment. In this paper we address the utilization of the concept of Multiagent Collaborative Computation  (MCC) in MO planning and execution. The multiagent will be applied to
replace all MO’s supporters such as the sensors, the field commanders to commanding officer’s staffs that will be collaborating to obtain comprehensive information to the commanding officer as the basis for quick and accurate decision-making in order to accomplish the mission
successfully.

Knowledge-Growing System (KGS) is a system that is capable of growing its knowledge along with the accretion of information as the time passes and uses it as the basis for making a decision. The knowledge growing mechanism in the system is performed by using Observation Multi-time A3S (OMA3S) information-inferencing fusion method. The smallest structure of KGS is an intelligent agent. In this paper we show that the knowledge grows by an agent in an intelligent agent network displays a knowledge-sharing mechanism. The ultimate knowledge resulted from this knowledge-sharing mechanism will be used as the basis for selecting the most appropriate combination of agents that is capable of delivering the desired information in this intelligent agents network

The essential matter of Artificial Intelligence (AI) is how to build an entity that mimics human intelligence in the way of learning of a phenomenon in a real life to gain knowledge of it and uses the knowledge to solve problems related to it. Based on the findings of intelligent characteristic displayed by the human brain in growing and generating new knowledge by fusing information perceived by sensory organs, we develop brain-inspired Knowledge-Growing System (KGS) that is, a system that is capable of growing its knowledge along with the accretion of information as the time passes. The essential matter of KGS is knowledge-growing method which is based on a new algorithm called Observation Multi-time A3S (OMA3S) information-inferencing fusion method. In this paper we deliver the development of KGS along with some examples of KGS application to a real-life problem. Based on the state-of-the-art of AI and approaches to construct OMA3S method as KG method as well as validations to assess the system performance, we state that brain-inspired KGS is a novel field in AI.

Military Operation (MO) is a very complex problem where many parties are involved to accomplish the planned mission. In a sustain mission that needs a full attention and may be running for a long period, there are possibilities that the commanding officer and his supporting staffs as well as the field commanders become exhausted and this situation can affect the mission’s objective accomplishment. In this paper we address the utilization of the concept of Multiagent Collaborative Computation (MCC) in MO planning and execution. The multiagent will be applied to replace all MO’s supporters such as the sensors, the field commanders to commanding officer’s staffs that will be collaborating to obtain comprehensive information to the commanding officer as the basis for quick and accurate decision-making in order to accomplish the mission successfully

Joint Military Operation (JMO) is a very complex problem where many parties are involved to accomplish the planned mission. In a sustain mission that needs a full attention and may be running for a long period, there are possibilities that the commanding officer and his supporting staffs as well as the field commanders become exhausted and this situation can affect the mission’s objective accomplishment. In this paper we address the utilization of the concept of Multiagent Collaborative Computation (MCC) in JMO planning and execution. The multiagent will be applied to replace all JMO’s supporters such as the sensors, the field commanders to commanding officer’s staffs that will be collaborating to obtain comprehensive information to the commanding officer as the basis for quick and accurate decision-making in order to accomplish the mission successfully.  The knowledge of the comprehensive information will be obtained by using Observation Multi-Time Arwin-Adang-Aciek-Sembiring (OMA3S) information-inferencing fusion method.  A simple scenario of a JMO mission will be delivered to show how these paradigm and method work together

The field of Artificial Intelligence (AI) has been progressing very advanced since its introduction in 1950s. The essential matter of AI is how to build an entity that mimics human intelligence in the way of learning of something to gain knowledge of it and use the knowledge to solve problems related to it. In
this paper we review a novel field on AI called Knowledge-Growing System (KGS) that is, a system that is capable of growing its knowledge along with the accretion of information as the time passes. KGS is developed from our observation of how human’s brain gain new knowledge by fusing information
gathered and delivered by human’s sensory organs. The result of the observation is a new knowledgegrowing algorithm called as Observation Multi-time A3S (OMA3S) information-inferencing fusion method. Some examples will be delivered to show the application of KGS to real-life problems

Genetic Regulatory System (GRS) is a system that regulates when and in what form a gene or genes will be expressed. It also determines the character of a gene, namely its form, function, and behavior. The expression of a gene can be guessed by analyzing the phenomenon occurred in obtaining the expression namely the character of the genes interaction in a series of time. For this purpose, in this paper we address a
novel approach for analyzing the mentioned phenomenon called A3S (Arwin-Adang-Aciek-Sembiring) analyzer. The method is based on probability computation and the outcome of the computation is the Degree of Certainty (DoC) regarding the guessed phenomenon. We have carried out experiments to validate the analyzing method to a yeast gene database. The experiment results show that the A3S analyzer is a promising
means for analyzing genes behavior in GRS as well as a significant contribution in computational biology field.

In Network-Centric Warfare paradigm, the key to win the war is information. In order to obtain as much as possible information of the battlefield situation, the commandant orders his units to deploy sensors in all strategic areas. The obtained information can be used by the commandant as decision maker to make a quick and correct strategic decision to win the war. Managing information that is coming from strategicallydistributed sensors is the key to commandant’s decision cycle and can become a big problem when the network gets larger. In this paper we propose and design a MultiAgent-based Information Fusion System (MAIFS) as one of approaches to cope with this problem. The system uses the advantage of information fusion in combining information obtained from the distributed sensors to produce more complete and comprehensive situational awareness information. The distributed sensors are treated as individual agents that form a network of agents called a multiagent system with information fusion capability. We will explain the steps in designing the MAIFS clearly by using a simple example of simple NCW scenario to give more understanding on the proposed system

To identify an object in the air, Radar transmits Identification Friend, Foe, or Neutral (IFFN) signal. The detected object then will be manually matched to the flight data. If the object detected by the Radar is unregistered then the object will be
identified as black flight. Problem in identifying this kind of object is if object’s Radar Cross Section (RCS) is not in accordance with its speed. Only expert observer that is able to identify this object’s identity by using his experiences. In order that black flight object identification process can be quickly
and accurately done, an Identification Friend, Foe, or Neutral (IFFN) system based Adaptive Resonance Theory 1 Neural Network (ART1-NN) and information fusion is proposed and designed. The ART1-NN is employed because of its capability as a direct-access pattern matcher. The system uses the object’s Radar Cross Section (RCS) and speed as identifier data. The ART1-NN matches the RCS and speed data of detected object
to the data it already learned. Final identification process is done by fusing information resulted from the ART1-NN matching process by using voting and Boolean AND methods to obtain final estimated identity of observed object

To have a system than can think, act, and decide like that of human does has been a challenge for scientists, researchers, and practitioners since the invention of computer and electronic devices which support it. At the heart of a computer-based system is a matter that has task to process information and based on the processed information, the computer will act or decide on that information. At the next level, it is needed a system that is able to process information intelligently. In this paper, we introduce our research on developing a new model of information system extracted from human brain mechanism that we call as information-inferencing fusion. The approach on this model is a part of our research on Cognitive Artificial Intelligence to create a cognitive intelligent system

Nation sovereignty is an absolute value that cannot be negotiated. That is why any sovereignty breach must be handled as soon as possible before it influences the Nation Defence by means of correct decisions or actions. In order to be able to make a decision in a quick manner, the decision maker requires comprehensive information regarding the current situation. Problems arise in how to manage the flow of information that comes from diverse distributed sensors deployed in a vast area, so it can be delivered to the decision maker as comprehensive information that covers the information gathered by the sensors and be used as the basis for decision making in a quick manner.

Knowledge Growing System (KGS) is a novel perspective in Artificial Intelligence (AI) which is aimed to emulate how the human brain obtains new knowledge from information delivered by human sensory organs. The new knowledge is then used as the basis for making estimation in the future of the phenomenon being observed as the basis for the most appropriate decision or action that will be decided or taken. In this paper we address the application of KGS to infer the behavior of genes interaction in Genetic Regulatory System (GRS) in order to estimate their behavior in the subsequent interaction time. For this purpose we model the genes as multi-agent that performs collaborative computations in Multiagent Collaborative Computation (MCC) paradigm. The knowledge regarding the genes behavior is obtained by applying a novel information-inferencing fusion method called Observation Multi-time Arwin-Adang-Aciek-Sembiring (OMA3S). In order to show how KGS works in MCC framework, we use yeast genes-interaction values as the case study

Knowledge Growing System (KGS) is a novel perspective in Artificial Intelligence (AI) which is aimed to emulate how the human brain obtains new knowledge from information delivered by human sensory organs. The new knowledge is then used as the basis for making estimation in the future of the phenomenon being observed as the basis for the most appropriate decision or action that will be decided or taken. In this paper we address the application of KGS to infer the behavior of genes interaction in Genetic Regulatory System (GRS) in order to estimate their behavior in the subsequent interaction time. For this purpose we model the genes as multi-agent that performs collaborative computations in Multiagent Collaborative Computation (MCC) paradigm. The knowledge regarding the genes behavior is obtained by applying a novel information-inferencing fusion method called Observation Multi-time Arwin-Adang-Aciek-Sembiring (OMA3S). In order to show how KGS works in MCC framework, we use yeast genes-interaction values as the case study

On disaster prone-areas, the exitences of autonomous vehicle is need to provide monitoring process as an early detect for any possibility of disaster. An appropriate processor is needed to support autonomous vehicle in order to gathering information related to condition of monitoring area. Cognitive Processor is a processor which have ability to emulate how human brain work to process information by gathering information from the environment and take the inference from the information that have received. To design Cognitive processor, appropriate method is needed to support the performance of this processor. Knowledge Growing System (KGS) is a new development of Artificial Intelligence that have ability to growing knowledge base on information which have been gathered as time passes. KGS concept emerge by observing mechanism of human brain when doing information fusion to gain new knowledge. This paper aimed to deliver concept of designing a cognitive processor which suport by KGS Algorithm and fusion information method.

Genetic Regulatory System (GRS) and Deoxyribonucleic Acid (DNA) are two important matters in the development and functioning of organisms. GRS is a mechanism for determining when, where, in what form and to which extent a gene or genes will be expressed, while DNA is the essential part of a gene. In this paper we address the utilization of Multiagent Collaborative Computation (MCC) to model the genes' interactions in GRS. The aim of the modeling is to examine each gene contribution to the interactions in a series of times manner. By having the knowledge of their interactions, we can infer their behavior and make estimations in order to obtain better interactions' products in the future.

Knowledge growing is one of intelligence characteristics possessed by human brain. In this paper we review some fundamental theories that are appropriate for emulating this kind of intelligence in order to develop an intelligent system in Artificial Intelligence (AI) field, called brain-inspired Knowledge-Growing System (KGS). The development of this system is approached from various fields, namely psychological, mathematical, social, and electrical engineering and informatics fields. Based on the review results, we have built this system along with mechanism for growing the knowledge that consists of a model of Human Inference System (HIS), Sense-Inference-Decide and Act (SIDA) cycle, and the mathematical formulation for growing the knowledge called Observation Multi-time Arwin-Adang-Aciek-Sembiring (OMA3S) information-inferencing fusion method. In conclusion, brain-inspired KGS is a cognitive agent which is equipped with knowledge growing mechanism as its intelligent characteristic.

Knowledge Growing System (KGS) is a novel perspective in Artificial Intelligence (AI) which is aimed to emulate how the human brain obtains new knowledge from information delivered by human sensory organs. The new knowledge is then used as the basis for making estimation in the future of the phenomenon being observed as the basis for the most appropriate decision or action that will be decided or taken. In this paper we address the application of KGS to infer the behavior of genes interaction in Genetic Regulatory System (GRS) in order to estimate their behavior in the subsequent interaction time. For this purpose we model the genes as multi-agent that performs collaborative computations in Multiagent Collaborative Computation (MCC) paradigm. The knowledge regarding the genes behavior is obtained by applying a novel information-inferencing fusion method called Observation Multi-time Arwin-Adang-Aciek-Sembiring (OMA3S). In order to show how KGS works in MCC framework, we use yeast genes-interaction values as the case study

Genetic Regulatory System (GRS) and Deoxyribonucleic Acid (DNA) are two important matters in the development and functioning of organisms. GRS is a mechanism for determining when, where, in what form and to which extent a gene or genes will be expressed, while DNA is the essential part of a gene. In this paper we address the utilization of Multiagent Collaborative Computation (MCC) to model the genes' interactions in GRS. The aim of the modeling is to examine each gene contribution to the interactions in a series of times manner. By having the knowledge of their interactions, we can infer their behavior and make estimations in order to obtain better interactions' products in the future.