Knowledge Growing System

A system employing artificial intelligent (AI) is currently popular to be developed to help people solving their problems, especially for problems that need skill of reasoning. Furthermore, knowledge growing system (KGS) is a new approach in AI environment which the system has a growing knowledge that can be learnt from the past. In this paper, a KGS-based system is applied to control electric source utilization in Nemberala Hybrid Power Plant. Electric source that will be used is controlled by inference information input from sensors which are processed in KGS. The result is Degree of Certainty (DoC) of probability electric source used, such as renewable energy, fossil energy, and Hybrid energy. From DoC, the source that should be used can be decided properly by the system.

Abstrak. Misalkan terdapat graf G = (V; E) dan W V (G), dimana jWj = K, dan W = fv 1 ; v 2 ; ; v g. Representasi metrik dari titik v 2 V terhadap W adalah r(v j W) = (d(v; v 1 k ); d(v; v 2 ); ; d(v; v )). Himpunan W dikatakan sebagai resolving set di G jika untuk setiap pasangan dari titik-titik berbeda u; v 2 V , r(u j W) 6 = r(v j W). Dimensi metrik dari G adalah kardinalitas minimum dari resolving set untuk G dan dinotasikan dim(G). Graf (K n k P ) adalah graf hasil kali Kartesius antara graf lengkap (K n ) dengan n titik dan graf lintasan (P adalah graf yang diperoleh dari graf (K dengan cara menghubungkan titik v ij n m P di (K n m P m ) dengan m titik. Graf (K n 1 P ) dengan nm titik dan graf lengkap K m ) ke titik u , yang merupakan salinan ke-ij dari graf K 1 ij , untuk 1 i n dan 1 j m. Pada paper ini dikaji kembali makalah [4] yang membahas tentang penentuan dim((K n P m ) K untuk n 3 dan m 2.

We study auctions with additive valuations where agents have a limit on the number of goods they may receive. We refer to such valuations as capacitated and seek mechanisms that maximize social welfare and are simultaneously incentive compatible, envy-free, individually rational, and have no positive transfers. If capacities are infinite, then sequentially repeating the 2nd price Vickrey auction meets these requirements. In 1983, Leonard showed that for unit capacities, VCG with Clarke Pivot payments is also envy free. For capacities that are all unit or all infinite, the mechanism produces a Walrasian pricing (subject to capacity constraints). Here, we consider general capacities. For homogeneous capacities (all capacities equal) we show that VCG with Clarke Pivot payments is envy free (VCG with Clarke Pivot payments is always incentive compatible, individually rational, and has no positive transfers). Contrariwise, there is no incentive compatible Walrasian pricing. For heterogeneous capacities, we show that there is no mechanism with all 4 properties, but at least in some cases, one can achieve both incentive compatibility and envy freeness.

Suatu pelabelan total sisi ajaib pada graf G dengan p merupakan banyak titik pada graf G dan q merupakan banyak sisi pada graf G adalah suatu fungsi bijektif f : V (G) ∪ E(G) → {1, 2, 3, · · · , p + q} sedemikian sehingga f(u) + f(v) + f(uv) = k, untuk setiap uv ∈ E(G) dengan k konstanta. Fungsi f dikatakan sebuah pelabelan total sisi ajaib super dari graf G jika f : V (G) → {1, 2, 3, · · · , p}. Graf prisma berekor (Xm,n) adalah graf yang dibentuk dari mC3 dengan menghubungkan dua titik yang bersesuaian di C3 dan menambahkan sebanyak n buah cabang pada titik-titik di C3 terluar. Graf Prisma Berekor (Xm,n) memiliki pelabelan total sisi ajaib super dengan konstanta k = 9(m + n).kata kunci: Pelabelan Total Sisi ajaib, Super, Fungsi bijektif, Graf prisma berekor, konstanta ajaib.Diterima: Direvisi: Dipublikasikan :Kata Kunci: Pelabelan Total Sisi ajaib super, Graf prisma berekor, konstanta ajaib.

We study auctions with additive valuations where agents have a limit on the number of goods they may receive. We refer to such valuations as {\em capacitated} and seek mechanisms that maximize social welfare and are simultaneously incentive compatible, envy-free, individually rational, and have no positive transfers. If capacities are infinite, then sequentially repeating the 2nd price Vickrey auction meets

We study auctions with additive valuations where agents have a limit on the number of goods they may receive. We refer to such valuations as capacitated and seek mechanisms that maximize social welfare and are simultaneously incentive compatible, envy-free, individually rational, and have no positive transfers. If capacities are infinite, then sequentially repeating the 2nd price Vickrey auction meets these requirements. In 1983, Leonard showed that for unit capacities, VCG with Clarke Pivot payments is also envy free. For capacities that are all unit or all infinite, the mechanism produces a Walrasian pricing (subject to capacity constraints). Here, we consider general capacities. For homogeneous capacities (all capacities equal) we show that VCG with Clarke Pivot payments is envy free (VCG with Clarke Pivot payments is always incentive compatible, individually rational, and has no positive transfers). Contrariwise, there is no incentive compatible Walrasian pricing. For heterogeneous capacities, we show that there is no mechanism with all 4 properties, but at least in some cases, one can achieve both incentive compatibility and envy freeness.

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%.

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 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

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

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

As the most perfect God’s creation, human has grandeur in making inferencing from data and or information gathered by his/her multi-sensing organ regarding the phenomena in his/her surrounding
environment. Data or information obtained from one source can only give him/her partial inferencing. By fusing or combining partial inferencing, he/she can have comprehensive inferencing regarding the
phenomena being occurred and can use this inferencing to predict or estimate the situation that can probably occur in the future. A kind of natural phenomena is global warming that has been a very important issue along with people consciousness to preserve the Earth survivability for their next generations.

Considering that, in this paper we propose a novel information inferencing fusion for global warming agents called A3S (Arwin-Adang-Aciek-Sembiring). A3S method is an enhancement of Bayes
inference method that is lack of capability when faced with multi-hypothesis multi-parameter problems such as global warming phenomenon. Global Warming Agent (GWA) is a thing that emulates human intelligent and has tasks to observe the global warming parameters. The fused information obtained by the GWA is an estimation or prediction of climate changes from time to time, so the people can have situational awareness regarding the situation being occurred and situation that can probably occur in the future.

This paper presents a new experience concerning a practical laboratory for Digital Logic Design (DLD) that can be accessed from anywhere via the Internet. Our approach was to design a virtual laboratory, using a multimedia platform, namely DigiLab which can be access from anywhere via the Internet. DigiLab run as an online application where a user needs to have internet connection and user account before access to the application. Virtual laboratory occasionally replaces traditional classrooms due to rapid advances in computer technologies. One of the engineering subjects, Digital Logic course is regarded as an important core requirement in Information Technology (IT) department. The IT student needs to study about logic for hardware purpose in IT. The idea of digital logic virtual lab is to allow students in IT to understand the logic circuit behavior and practice the skills of designing the circuit board with different techniques and instruments. Due to costly traditional laboratory, the DigiLab is designed to fulfill the needs of the knowledge for students.

This paper presents a new experience concerning a practical laboratory for Digital Logic Design (DLD) that can be accessed from anywhere via the Internet. Our approach was to design a virtual laboratory, using a multimedia platform, namely DigiLab which can be access from anywhere via the Internet. DigiLab run as an online application where a user needs to have internet connection and user account before access to the application. Virtual laboratory occasionally replaces traditional classrooms due to rapid advances in computer technologies. One of the engineering subjects, Digital Logic course is regarded as an important core requirement in Information Technology (IT) department. The IT student needs to study about logic for hardware purpose in IT. The idea of digital logic virtual lab is to allow students in IT to understand the logic circuit behavior and practice the skills of designing the circuit board with different techniques and instruments. Due to costly traditional laboratory, the DigiLab is designed to fulfill the needs of the knowledge for students.

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.

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

Probability theory is a matured mathematical technology that has been applied not only in science but also in engineering fields. In this paper we address our review on the potentiality of the application of probability theory to Knowledge Growing System (KGS), that is, a system that has a capability to grow its knowledge along with the accretion of knowledge it receives as the time passes. KGS is a new perspective in Artificial Intelligence (AI) which is aimed to mimic the way of human thinks which is full of probability in performing environment
assessment in order to obtain new knowledge for estimating an occurred phenomenon in his environment. In this paper, we also present our current research on computational probability
method for KGS called as A3S (Arwin-Adang-Aciek-Sembiring) information-inferencing fusion that can be considered as a significant contribution to electrical and informatics
engineering field as well as to mathematics field.

Information fusion is a technique in combining data or information to estimate or to predict various entity states. The entities can be in physical or non-physical form. The human multi-sensory system capabilities in perceiving entities state in his/her environment inspires the development of the information fusion. The data or information detected by the human multi-sense organ will be fused in his/her cognitive system to obtain comprehensive and more accurate information to give a situational awareness regarding recent
situation or situation that possibly occurs in the future.
This human’s extraordinary multi-source information fusion capability is then emulated to a computer system as part of an information system to help decision maker makes an inference before making a decision regarding current situation or situation that possibly occurs in the future. Advantage raised
from information fusion is more accurate, comprehensive, and reliable information in order to avoid uncertainty in making a decision. This paper presents application of multi-source information fusion to decision support system that is called as Information Fusion System for Decision Support (IFSDS). An
example of the use of IFSDS in producing comprehensive information for planning a logistic dropping will be presented as well as other important aspects in designing this 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

Knowledge-growing is a new approach in Cognitive Artificial Intelligence which efforts to emulate human intelligence exhibited by the mechanism occured within human brain in constructing new knowledge based on new information when interacting with the environment. Knowledge-growing methods and applications has been intensively studied and researched at Cognitive Artificial Intelligent Research Group (CAIRG) of the School of Electrical Engineering and Informatics (SEEI) of Institut Teknologi Bandung (ITB) since 2006. These efforts try to combine approaches in Artificial Intelligence, cognitive science, and cognitive engineering combining them with probabilistic techniques in developing systems which can perform human intelligence for real-world applications. In this paper, some of the research results will be delivered and some application examples in diverse fields such as methods of knowledge-growing, probabilistic cognitive robot, and predicting genes behavior in a Genetic Regulatory System (GRS) are also given in order to disseminate our researchs’ benefits to human kind

We study auctions with additive valuations where agents have a limit on the number of goods they may receive. We refer to such valuations as capacitated and seek mechanisms that maximize social welfare and are simultaneously incentive compatible, envy-free, individually rational, and have no positive transfers.

A system employing artificial intelligent (AI) is currently popular to be developed to help people solving their problems, especially for problems that need skill of reasoning. Furthermore, knowledge growing system (KGS) is a new approach in AI environment which the system has a growing knowledge that can be learnt from the past. In this paper, a KGS-based system is applied to control electric source utilization in Nemberala Hybrid Power Plant. Electric source that will be used is controlled by inference information input from sensors which are processed in KGS. The result is Degree of Certainty (DoC) of probability electric source used, such as renewable energy, fossil energy, and Hybrid energy. From DoC, the source that should be used can be decided properly by the system.

Keywords: knowledge growing; hybrid power; energy management