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Fig. 14. Influence graph of KGS-based MCC behaviour at f.,,

Figure 14 Influence graph of KGS-based MCC behaviour at f.,,

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Fig. 1. The concept of agent along with its information-inferencing fusion capability
Fig. 1. The concept of agent along with its information-inferencing fusion capability
Fig. 3. The concept of MCC (Sumari, 2010) approach. By combining the two definitions previously explained, we define “collaborative computation” as a calculation on quantities done by a group of agent or multiagent that works together to achieve common or joint goals (Ahmad, Sumari, & Zubir, 2009) as depicted in Fig. 2. In an MCC scheme, each agent in the collaborative framework performs its own tasks to achieve its own goals. The goals achieved by each agent actually are partial parts of joint goal, which is the combination of partial goals. In simple word, the system is given a problem along with the goal that has to be achieved. The problem is then divided into several sub-problems that have to be solved by the agents. In achieving the sub-goals, the agents perform computation in parallel. The achieved sub-goals are then combined to become a single comprehensive goal that is called as joint goal (Sumari & Ahmad, 2009). c. The Merits of Multiagent Collaborative Computation Approach (Sumari, 2010)
Fig. 3. The concept of MCC (Sumari, 2010) approach. By combining the two definitions previously explained, we define “collaborative computation” as a calculation on quantities done by a group of agent or multiagent that works together to achieve common or joint goals (Ahmad, Sumari, & Zubir, 2009) as depicted in Fig. 2. In an MCC scheme, each agent in the collaborative framework performs its own tasks to achieve its own goals. The goals achieved by each agent actually are partial parts of joint goal, which is the combination of partial goals. In simple word, the system is given a problem along with the goal that has to be achieved. The problem is then divided into several sub-problems that have to be solved by the agents. In achieving the sub-goals, the agents perform computation in parallel. The achieved sub-goals are then combined to become a single comprehensive goal that is called as joint goal (Sumari & Ahmad, 2009). c. The Merits of Multiagent Collaborative Computation Approach (Sumari, 2010)
Fig. 4. A simplified illustration of human inference system (Sumari et.al, 2010b)
Fig. 4. A simplified illustration of human inference system (Sumari et.al, 2010b)
Fig. 5. The mechanism of growing the knowledge in KGS as the representation of Fig. 4 (Sumari et.al, 2009a)
Fig. 5. The mechanism of growing the knowledge in KGS as the representation of Fig. 4 (Sumari et.al, 2009a)
with P(y/) is New-Knowledge Probability Distribution (NKPD), P(v/) is information j from sensori, while i=1,...,6 is the number of sensor and j= 1.,...,4 is the number of fused information. The P(yi) and y/« is the representation of “fused information” of the information delivered from multi-sensor. The word “estimated” means the selected fused information is the most likely inferencing/ knowledge from all available inferencing/ knowledge given information from multi-sensor. P(Y), na. i8 the largest value of P(yi) that is called as DoC and ©J...]= max [...]. Term Ply?) defines NKPD at Jj=1,..,.m yvy-—1. Next observations on the same phenomenon will result in new NKPD at next time series and these NKPD will form NKPD over Time (NKPDT) as presented in (4) and (5).
with P(y/) is New-Knowledge Probability Distribution (NKPD), P(v/) is information j from sensori, while i=1,...,6 is the number of sensor and j= 1.,...,4 is the number of fused information. The P(yi) and y/« is the representation of “fused information” of the information delivered from multi-sensor. The word “estimated” means the selected fused information is the most likely inferencing/ knowledge from all available inferencing/ knowledge given information from multi-sensor. P(Y), na. i8 the largest value of P(yi) that is called as DoC and ©J...]= max [...]. Term Ply?) defines NKPD at Jj=1,..,.m yvy-—1. Next observations on the same phenomenon will result in new NKPD at next time series and these NKPD will form NKPD over Time (NKPDT) as presented in (4) and (5).
As previously mentioned in earlier section, the genes regulated by GRS will be represented by agents that will be working together in MCC paradigm. The experiments and the results delivered in this section are taken from (Sumari, 2010). We use data from (Zubir, 2009) namely yeast25-cho database as presented in Table 2. In this database, there are 25 genes from ACE2 to SIC1 as listed in column 1 with an interaction time interval from t-1 to t-17 or 17t times as shown in line 1. In order to give a view on the challenge in obtaining the knowledge regarding what genes are expressed in a given sample as well their behavior in a certain interaction time, the complexity of the data given in Table 3 is depicted in Fig. 6 and Fig. 7. The expressiveness of individual genes in 17t of interaction time is presented in Fig. 6, while the genes behavior in 17t of interaction time is presented in Fig. 7. The challenge in this case is to obtain knowledge from this phenomenon to answer the questions raised in Section 1.
As previously mentioned in earlier section, the genes regulated by GRS will be represented by agents that will be working together in MCC paradigm. The experiments and the results delivered in this section are taken from (Sumari, 2010). We use data from (Zubir, 2009) namely yeast25-cho database as presented in Table 2. In this database, there are 25 genes from ACE2 to SIC1 as listed in column 1 with an interaction time interval from t-1 to t-17 or 17t times as shown in line 1. In order to give a view on the challenge in obtaining the knowledge regarding what genes are expressed in a given sample as well their behavior in a certain interaction time, the complexity of the data given in Table 3 is depicted in Fig. 6 and Fig. 7. The expressiveness of individual genes in 17t of interaction time is presented in Fig. 6, while the genes behavior in 17t of interaction time is presented in Fig. 7. The challenge in this case is to obtain knowledge from this phenomenon to answer the questions raised in Section 1.
Table 2. Yeast25 database for validating KGS-based MCC In this research we use five kinds of yeast25 database as presented in Table 2.
Table 2. Yeast25 database for validating KGS-based MCC In this research we use five kinds of yeast25 database as presented in Table 2.
Obtaining Knowledge of Genes’ Behavior in Genetic Regulatory System by Utilizin Multiagent Collaborative Computation
Obtaining Knowledge of Genes’ Behavior in Genetic Regulatory System by Utilizin Multiagent Collaborative Computation
Fig. 6. The expressiveness of individual genes in 17t of interaction time (Sumari, 2010)
Fig. 6. The expressiveness of individual genes in 17t of interaction time (Sumari, 2010)
Multi-Agent Systems - Modeling, Interactions, Simulations and Case Studies
Multi-Agent Systems - Modeling, Interactions, Simulations and Case Studies
Fig. 8. The representation of genes of yeast25-cho by means agents in MCC structure
Fig. 8. The representation of genes of yeast25-cho by means agents in MCC structure
Table 4b. The conversion results of genes interaction values from Table 2b
Table 4b. The conversion results of genes interaction values from Table 2b
Table 4a. The conversion results of genes interaction values from Table 2a (continued to Table 4b) Obtaining Knowledge of Genes’ Behavior in Genetic Regulatory System by Utilizing Multiagent Collaborative Computation
Table 4a. The conversion results of genes interaction values from Table 2a (continued to Table 4b) Obtaining Knowledge of Genes’ Behavior in Genetic Regulatory System by Utilizing Multiagent Collaborative Computation
Fig. 12. Knowledge acquired by agents at t,
Fig. 12. Knowledge acquired by agents at t,
Obtaining Knowledge of Genes’ Behavior in Genetic Regulatory System by Utilizing Multiagent Collaborative Computation
Obtaining Knowledge of Genes’ Behavior in Genetic Regulatory System by Utilizing Multiagent Collaborative Computation
Fig. 13. Knowledge acquired by agents after 6t of interaction time Multi-Agent Systems - Modeling, Interactions, Simulations and Case Studies
Fig. 13. Knowledge acquired by agents after 6t of interaction time Multi-Agent Systems - Modeling, Interactions, Simulations and Case Studies
Fig. 15. Knowledge acquired by agents at t,,
Fig. 15. Knowledge acquired by agents at t,,
Obtaining Knowledge of Genes’ Behavior in Genetic Regulatory System by Utilizing Multiagent Collaborative Computation
Obtaining Knowledge of Genes’ Behavior in Genetic Regulatory System by Utilizing Multiagent Collaborative Computation
Fig. 17a. The ultimate knowledge acquired by the system regarding individual agents expressiveness after 17t of interaction time. The DoCs of the system’s knowledge are compared with the communication paths formed amongst agents
Fig. 17a. The ultimate knowledge acquired by the system regarding individual agents expressiveness after 17t of interaction time. The DoCs of the system’s knowledge are compared with the communication paths formed amongst agents
Obtaining Knowledge of Genes’ Behavior in Genetic Regulatory System by Utilizing Multiagent Collaborative Computation
Obtaining Knowledge of Genes’ Behavior in Genetic Regulatory System by Utilizing Multiagent Collaborative Computation
Multi-Agent Systems - Modeling, Interactions, Simulations and Case Studies
Multi-Agent Systems - Modeling, Interactions, Simulations and Case Studies
Obtaining Knowledge of Genes’ Behavior in Genetic Regulatory System by Utilizing Multiagent Collaborative Computation
Obtaining Knowledge of Genes’ Behavior in Genetic Regulatory System by Utilizing Multiagent Collaborative Computation
\ multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains.
\ multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains.
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