Distributed Task Allocation in Multi-Agent System Based on Decision Support Module
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(IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 9, 2010 Distributed Task Allocation in Multi-Agent System Based on Decision Support Module Sally M. El-Ghamrawy Ali I. El-Desouky Ahmed I. Saleh Computers and Systems Computers and Systems Computers and Systems Department, Department, Department, Faculty of Engineering, Faculty of Engineering, Faculty of Engineering, Mansoura University, Egypt Mansoura University, Egypt Mansoura University, Egypt Sally@mans.edu.eg email@example.com firstname.lastname@example.org Abstract-. A Multi-Agent System (MAS) is a branch of that are beyond their individual capabilities. There are some distributed artificial intelligence, composed of a number of modules must be considered in designing the Distributed distributed and autonomous agents. In MAS, an effective Multi-Agent Intelligent System (DMAIS) that help in coordination is essential for autonomous agents to reach their developing in the agent systems. There are many researches goals. Any decision based on a foundation of knowledge and considered some modules when designing multi-agent reasoning can lead agents into successful cooperation, so to achieve the necessary degree of flexibility in coordination, an systems (MAS) in different ways. Each autonomous agent in agent requires making decisions about when to coordinate and DMAIS must be able to decide how to behave in various which coordination mechanism to use. The performance of any situations, so in this paper our main concern is to propose an MAS depends directly with the right decisions that the agents efficient decision support module that helps in the made. Therefore the agents must have the ability of making improvement of DMAIS performance. right decisions. In this paper, we propose a decision support Agents have attractive characteristics like: autonomy, module in a distributed multi-agent system, which enables any reactivity, reasoning capability and social ability. These agent to make decisions needed for Task allocation problem; we characteristics ensure that agent-based technologies are propose an algorithm for Task Allocation Decision Maker responsible for enhancing the decision support system (TADM). Furthermore, a number of experiments were performed to validate the effectiveness of the proposed capabilities beyond the capabilities of the old model. Any algorithm (TADM)); we compare the efficiency of our active decision support can be facilitated by the autonomy, algorithms with recent frameworks. The preliminary results reactivity and social ability of agents. Furthermore, the demonstrate the efficiency of our algorithms artificial view of agents can contribute towards stronger collaborative relationships between a human and a decision Keywords: .Decision Making, Task allocation, Coordination Mechanism, support system. These enormous interests of researchers in Multi-Agent System (MAS) investigating the decision support in Multi-Agent Systems is I. INTRODUCTION due to the great benefits of combining the decision support technology and Agent-based technology with taking the The notion of distributed intelligent systems (DIS)  has advantage of the agent characteristics. In this sense, many been a subject of interest for number of years. A Multi-Agent researches [3-7] recognized the promise that agent -based system (MAS) is one of the main areas in the DIS. Any technologies holds for enhancing DSS capabilities. Multi-agent system consists of several agents capable of Decision support module is a vital module in the success of mutual interaction, with heterogeneous capabilities, that DMAIS framework, due to the fact that any decision based cooperate with each others to pursue some set of goals, or to on a foundation of knowledge and reasoning can lead agents complete a specific task. MAS used to solve problems which into successful cooperation. So the performance of any multi- are difficult or impossible for an individual agent or agent system depends directly with the right decisions that monolithic system to solve. Agents are autonomous the agents made. Obviously, complex decision making tasks programs which can understand an environment, take actions cannot be achieved by a single agent. Rather, it’s achieved by depending upon the current status of the environment using efforts coordination of multiple agents possess different sets its knowledge base and also learn so as to act in the future. In of expertise, attributes and assignment. This coordination order to solve complex problems agents have to cooperate among agents, which provide satisfactory solutions to and exhibit some level of autonomy. Agents cooperate with problems among agents, needs many decisions that agents are each other to solve large and complex collaborative required to make before this coordination can take place. So problems. Because the majority of work is completed through the Decision support module role is to allow agents to make distributing the tasks among the cooperative agents, the the decision needed, which can help in the improvement of decision support module is responsible on the most of the the DMAIS framework. In this paper, we extended the work work. So the pros and cons in decision support module done in DMAIS  by proposing a Decision support module in it, this module is concerned about taking the right directly affect on the success of tasks completion and decisions needed to allocate a specific task to a specific agent, problem solving. by using the Task Allocation Decision Maker sub-module In , we proposed a Distributed Multi-Agent Intelligent (TADM). So we spotted on these decisions due to the great System (DMAIS,) a general purpose agent framework, in importance for them in the improvement of agents’ which several interacting intelligent agents cooperate with coordination in DMAIS framework. The rest of the paper is some auxiliary agents to pursue some set of goals or tasks organized as following. Section 2 demonstrates the proposed 210 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 9, 2010 decision support module. Section 3 discusses the related work The Decision support module is activated when a decision is on task allocation in Multi-agent Systems. An algorithm for needed from the agent, we concerned about two main the Task Allocation Decision Maker (TADM) is proposed in decisions that the agent may face: (1) the decision needed to section 4, showing the scenario of our algorithm in allocating allocate a specific task to a specific agent, making an tasks to specific agent/s. Section 5 shows the experimental effective decision for the task allocation problem is a critical evaluation and the results obtained after implementing the job for any multi-agent system; it helps our DMAIS proposed algorithm. Section 6 summarizes major contribution framework to complete its tasks and missions through of the paper and proposes the topics for future research. cooperation among agents. The Task Allocation Decision Maker sub-module (TADM) is responsible for making this decision. (2) The decision needed to select appropriate II. DECISION SUPPORT MODULE coordination mechanism, when agents need to coordinate In the DMAIS framework , the decision support module with another agent/s to accomplish a specific task. takes the information collected in the coordination and The decision support module contains four main phases, as negotiation module, as shown in fig 1, so that it can help in shown in figure 2, first is the decision knowledge the decision support process. management that contains: The database: contains the data that directly related Management Module Reasoning Module Coordination Module to the decision problem (i.e. the performance measures, the values of nature states). Planning Module Negotiation Module The knowledge base: contains the descriptions for Scheduling Module roles and structures of document and some knowledge of Decision Support the problem itself, (i.e. Guide for how to select decision Module Execution Module alternatives or how to interpret outputs). The knowledge modeling: is a repository contains the decision problem formal models and the algorithms and Communication Module methodologies for developing outcomes from the formal Figure 1. The Decision Support Module in DMAIS Framework models. Also contains different process models, each model is represented as a set of process and event objects. Decision knowledge Management Knowledge Knowledge Database Base Modeling Agent State Evaluator Task State Evaluator Organizer Attributes estimator Demands estimator Data Capabilities estimator Urgency degree estimator Behaviors estimator Importance degree estimator Dependency estimator Relevant Relevant data from data from Tasks agents Data Interpreter Allocation Decision Decision making method identifier Maker Task Building the representation model Calculating the expected utilities of all possible alternative solutions Proper Action Selection C . M Selection Decision Maker C.M list Bonus Estimator Cost Estimator Probability of success estimator Calculate Maximum Switch procedure values Action Taker Module Result Evaluator Evaluator Figure 2. Architecture of the Decision Support Module 211 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 9, 2010 Second phase is the data organizer, which organizes Tasks allocation is defined, in , as the ability of agents to agent’s attributes using the agent state evaluator and self-organize in groups of agents in order to perform one or organizes task’s preferences by using task state evaluator The more tasks which are impossible to perform individually. In agent state evaluator estimates the attributes and capabilities our context, it is a problem of assigning responsibility and of agents, in our work we concerned on specific attributes problem solving resources to an agent. There are two main and characteristics of the agent that will help in the decision benefits for Minimizing task interdependencies in the making procedure, as shown in fig 3, where: coordination process between the agents: First, improving the problem solving efficiency by decreasing communication overhead among the agents. Second, improving the chances for solution consistency by minimizing potential conflicts. The issue of task allocation was one of the earliest problems to be worked on in Distributed Artificial Intelligence (DAI) research. In this sense, several authors studied the problem related to Task Allocation especially in MAS. The researches in task allocation can be classified in to two main parts, centralized and distributed, based on utility/cost functions. The researches that investigate the task allocation problem in a centralized manner: Zheng and Koenig  Figure 3. Attributes, Capabilities and behaviors of Agents presented reaction functions for task allocation to cooperative agents. The objective is to find a solution with a small team Agent attributes is defined as a tuple cost and each target to be assigned to the exact number of <AgentID(A), Addrs(A)> (1) different agents. This work assumed that there is a central Where AgentID(A) is the identity of the agent, Addrs(A) planner to allocate tasks to agents. Kraus et al.  proposed records the IP address of agent A. Agent Capability is defined an auction based protocol which enables agents to form as a tuple coalitions with time constrains. This protocol assumed each < Availability(A), Ability(A), Intensity(A)> (2) agent knows the capabilities of all others, and one manager is Where Availability(A) is the ratio of total number of responsible for allocating tasks to all coalitions. Pinedo  successful agents, capable of accomplishing the task, to the proposed a job shop scheduling treats the task allocation total number of agents in the system, mostly in a centralized manner, and also ignores the AVLB(T)= SUC(A) / TOT (A) (3) communication cost. There are many drawbacks in the centralized task allocation like single point failure and bad Ability(A) indicates number of agents capable of fulfilling scalability. To conquer these disadvantages, Task allocation the task. Intensity(A) refers to the number of tasks that the agents can accomplish per time unit. in distributed environments has also been investigated. Davis Agent Behaviors is defined as a tuple and Smith  was the first in investigating a classic < Task History(A), Active degree(A), Dependency(A)> (4) distributed task allocation in the multi–agent system using Contract Net Protocol (CNP), in which agents negotiate to Where task history(A) stores the number of accomplished assign tasks among themselves. Most of the subsequent tasks that the agent(A) performed. Active degree(A) indicates literature on distributed task allocation is based on either the activity degree of a specific agent, this degree varies from contract net protocol or auctions . The authors in  agent to another according to many parameters (e.g. number and  developed distributed algorithms with low of finished tasks, number of cooperation process). Agent communication complexity for forming coalitions in large- Dependency(A) is concerned with the Cooperation Degree of scale multi-agent systems. Abdallah and Lesser  provided an Agent(A) with respect to other agents in the system. a decision theoretic model in order to limit the interactions Third phase is divided into two main Sub-modules: (1)The between agents and mediators. Mediators in this research Task Allocation Decision Maker (TADM) ,this sub-module mean the agents which receive the task and have connections will be discussed in details in the next sections, and (2) The to other agents. Mediators have to decompose the task into Coordination Mechanism Selection Decision Maker subtasks and negotiate with other agents to obtain (CMSDM), that takes the decision needed to select commitments to execute these subtasks. However, their work appropriate coordination mechanism, when agents need to concentrated on modelling the decision process of a single coordinate with another agent/s to accomplish a specific task, mediator. Sander et al.  presented a scalable and it will be discussed in our future work. Both of them are distributed task allocation protocol. The algorithm adopted in responsible on making the proper decisions according to the this protocol is based on computation geometry techniques input obtained from the Data organizer phase. The module but the prerequisite of this approach is that agents’ and tasks’ evaluator phase is the fourth phase, it has two main goals: geographical positions are known. Weerdt et al.  first, take the action that the third phase decided to be taken. proposed a distributed task allocation protocol in social Second, evaluate the results of taking this action. networks. This protocol only allows neighbouring agents to help with a task which might result in high probability of III. RELATED WORK abandon of tasks when neighbours cannot offer sufficient resources. Dayong et al.  proposed an Efficient Task The task allocation in Multi-Agent Systems represent a Allocation Protocol (ETAP) protocol based on the Contract problem which occupied to a large extends the researchers of Net approach, but more suitable for dealing with task decision support and artificial intelligence until our days. allocation problems in P2P multi-agent systems with a 212 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 9, 2010 decentralized manner. It enables agents to allocate tasks not used the rough set theory for classifying these metrics to only to their neighbours but also to commit unfinished tasks obtain the decision of allocating tasks to agent/s. Then this to their neighbours for reallocation. In this way, the agents decision is tested, it may be one of three decisions: first, if the can have more opportunities to achieve solution of their tasks allocation cannot be done, in this situation the capabilities of Brahmi et al.  developed a decentralized and scalable agents and demands of tasks must be defined all over again, it method for complex task allocation for Massive Multi-Agent may encounter any kind of error. second, if the decision is to System, distributing the process of computing the optimal allocate the task to only one agent, then task priority must be allocation among all agents based on the hypothesis: non reviewed to check the task queue for each agent, and depend conflict will be generated in the task allocation processes. on this queue an action must be taken whether to delay, reject Indeed, while being based on its Galois Sub-Hierarchy (GSH) or execute this task. Third, if the decision is to allocate the and cooperation with other agents, each agent chooses the task to more than one agent, then the coordinated agents must appropriate sub-task that ensures the global allocation registered, and the sub-tasks must be distributed among those optimality. Cheng and Wellman  used a market based coordinated agents according to the algorithm in figure 5. protocol for distributed task allocation. TADM algorithm 1. Agent Ai randomly select task T 2. AinCharge= Ai IV. TASK ALLOCATION DECISION MAKER (TADM) 3. For each a(i) in G 4. Ai=Send request ( ) The task allocation Decision Maker (TADM) main goal is 5. Ai=Wait response( ) to take the proper decisions of allocating tasks to the right 6. If time waited > expired time then 7. Exit for agents. The first step in allocating tasks to specific agents is 8. End if to take the decision whether the agents are capable of 9. Ai=Receive response( ) 10. Ai=Process response( ) executing part or the entire task. The allocation decisions are 11. Ai=Store response() made independently by each agent 12. Max-value=0 13. If helpfulness-value> Max-value then The decisions needed for the Task allocation problem are 14. Max-value = helpfulness-value mainly concerned about allocating the tasks to number of 15. Nominated-agent= a(i) 16. End if agents, whether these agents can complete its tasks by 17. Next themselves or not. If agents can’t achieve the task by 18. Xx: 19. Ai=Check (Nominated-agent( )) themselves, they attempt to give a decision to specify other 20. If helpfulness-value(Ai)< Nominated-agent then agents which have the appropriate capabilities and assign the 21. Ai= send Response(Nominated-agent) 22. If reply (Nominated-agent) =1 then task, or part of the task, to those agents. Fig 4 depicts the 23. AinCharge= Nominated-agent scenario of allocating tasks to specific agent/s in the TADM. 24. Else 25. Ai= find_scnd_highst( ) 26. Go to xx 27. End if 28. Else 29. AinCharge= Ai 30. End if Figure 5: The Pseudo code for TADM algorithm V. EXPERIMENTS EVALUATION To evaluate the performance of TADM, we compare it with Efficient Task Allocation Protocol ETAP  and with the Greedy Distributed Allocation Protocol (GDAP) . In order to validate the effectiveness of TADM algorithm and compare it with ETAP and GDAP two experiments are performed, as shown in figure 5; each experiment has its own goal and settings. In each experiment to evaluate the experiment results two metrics are evaluated the Efficiency Ratio and Run Time, which can be defined as follows: Figure 4: The Proposed algorithm used in TADM First the capabilities and behaviors of agents must be defined, and also the demands of tasks, it’s taken from the data organizer as an input to the TADM The metrics that facilitate the decision making process must be evaluated, and then we Figure 5: The experiments of Evaluating TADM algorithm 213 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 9, 2010 The Efficiency Ratio is the ratio between summation From figure 7, it is shown that when the number of agents efficiency of finished tasks and the total efficiency expected is increasing, the Efficiency Ratio of TADM is much higher of tasks. The efficiency of a task can be calculated as follows: and more stable than of ETAP and GDAP that is continually descending with the increasing of agents. Figure 8 shows the Run Time of TADM, GDAP and ETAP Where: when the number of agents in the network change. It can be is the rewards gained from successfully finishing the noticed that ETAP spends more time when there are more agents in the network, this is because ETAP make many task. is the resources required for accomplishing the reallocation steps which results in time and communication task. overhead rising. On the other hand, the time consumption of Run Time is the time of performing TADM algorithm in the GDAP is steady during the entire test process and keeps a network under pre-defined settings. The unit of Run Time is lower level than ETAP, this because GDAP relies on millisecond. To investigate the effects of TADM, and neighbouring agents only. compare it with ETAP and GDAP, a multi-agent system has been implemented to provide a testing platform. The whole system is implemented on a 6 Pc’s with an Intel Pentium 4 processor at 300GHz, with 3GB of Ram, connected with network Ethernet 512Mbps. A network of cooperative agents is designed, in which most agent team are connected to each other, the generation of this network can follow the approach proposed in . For each experiment, there are unified settings have to be specified. Figure 6 shows the TADM when it’s activated, and begins to perform its roles. Figure 8: The Run Time on different number of agents While TADM avoid these two drawbacks from the recent systems, and this experiment shows that the TADM performance is faster than that of GDAP and ETAP. In Experiment 2: The main goal of this experiment is to test the influence of team grouping on TADM (i.e. show how different average number of agent’s team influences the performance of TADM) and compare it with ETAP and GDAP, as shown in figure 9 and figure 10, using same environment and settings, as follows: The average number of agent’s team is fixed at 8. The number of agents and tasks are 50 and 30 separately. Figure 6: Screen shot of the TADM GUI The average number of resources for each type is 30 and the average number of resources required by each task is In Experiment 1: The main goal of this experiment is to also 30. demonstrate the scalability of TADM algorithm and compare The number of resources types is 5. it with ETAP and GDAP, as shown in figure 7 and figure 8, using same environment and settings, as follows: The average number of agent’s team is fixed at 8. The number of agents range from 100 to 600, depending on the specific test. The number of tasks is range from 60 to 360. The number of resources types is range from 10 to 60. Figure 9: The Efficient Ratio on different number of agent’s team Figure 9 demonstrated that The Efficiency Ratio of TADM is much higher and more stable than that of GDAP and ETAP. GDAP performance is very low this is because task allocation in GDAP is only depending on neighbours of the agent. On the other hand, ETAP has better performance because it relies Figure 7: The Efficient Ratio on different number of agents 214 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 9, 2010 not only on neighbours of the agent, but also other agents if  Song Qiang, Lingxia Liu, "The Implement of Blackboard-Based Multi- agent Intelligent Decision Support System," iccea, vol. 1, pp.572-575, needed. 2010 Second International Conference on Computer Engineering and The TADM has the higher performance and more stable than Applications, 2010 GDAP and ETAP, these results ensures and validates our  Rustam and Bijan, “Pluralistic multi-agent decision support system: a algorithm. framework and an empirical test, ” Information & Management, vol. 41, Sept., 2004, pp. 883-898.  Wang Fuzhong, "A Decision Support System for Logistics Distribution Network Planning Based on Multi-agent Systems," dcabes, pp.214-218, 2010 Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Science, 2010  Tosic, P.T., Agha, G.A.: Maximal Clique Based Distributed Group Formation For Task Allocation in Large-Scale Multi-Agent Systems. In: Ishida, T., Gasser, L., Nakashima, H. (eds.) MMAS 2005. LNCS (LNAI), vol. 3446, pp. 104–120. Springer, Heidelberg (2005).  X. Zheng and S. Koenig. Reaction functions for task allocation to cooperative agents. In Proceedings of 7th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2008), pages 559–566, Estoril, Portugal, May 2008  S. Kraus, O. Shehory, and G. Taase. Coalition formation with uncertain Figure 10: The Run Time on different number of agents’ team heterogeneous information. In Proceedings of 2th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2003), pages 1–8, Melbourne, Australia, July 2003. The Run Time of GDAP, ETAP, TADM in different number  Pinedo, M., “Scheduling: Theory, Algorithms and Systems”, Prentice of agents’ team is depicts figure 10. It’s obviously that the Hall, 1995. Run Time of ETAP is higher than that of GDAP. As ETAP  Davis, R., and Smith, R., “Neogitiation as a metaphor for distributed has to reallocate tasks when resources from neighbours are problem solving,” Artificial Intelligence, 20, 63–100, 1983.  R. McAfee, and J. McMillan, “Auctions and bidding,” Journal of insufficient this lead to increase the reallocation steps and Economic Literature, 25, 699–738, 1987. more time spending. While GDAP is steady due to its  K. Lerman and O. Shehory. Coalition formation for largescale considering for only neighbours which could decrease the electronic markets. In Proceedings of 4th International Conference on time and communication cost during task allocation process. Multi-Agent Systems, pages 167–174, Boston, Massachusetts, USA, July 2000. IEEE Computer Society. But TADM takes the benefits of agent’s team and also uses  O. Shehory and S. Kraus. Methods for task allocation via agent any other agent if needed, and in the same time reduces the coalition formation. Artificial Intelligence, 101(1-2):165–200, May steps taken to allocate the task which lead to decrease the 1998 time and communication cost during task allocation process.  S. Abdallah and V. Lesser. Modeling task allocation using a decision theoretic model. In Proceedings of 4th Automous Agents and So this is why TADM has better Run Time that GDAP and Multiagent Systems (AAMAS 2005), pages 719–726, Utrecht, ETAP. Netherlands, July 2005.  P. V. Sander, D. Peleshchuk, and B. J. Grosz. A scalable, distributed VI. CONCLUSION AND FUTURE WORK algorithm for efficient task allocation. In Proceedings of 1st International Conference on Autonomous Agents and Multiagent In this paper, a decision support module is proposed which Systems (AAMAS 2002), pages 1191–1198, Bologna, Italy, July 2002. enables any agent to make decisions needed, focused on two  M. D. Weerdt, Y. Zhang, and T. Klos. Distributed task allocation in main decisions: first, is the decision needed to allocate a social networks. In Proceedings of 6th Automous Agents and specific task to a specific agent. Second, the decision needed Multiagent Systems (AAMAS 2007), pages 500–507, Honolulu, Hawaii, USA, May 2007. to select appropriate coordination mechanism. And a survey  Dayong Ye, Quan Bai, Minjie Zhang, Khin Than Win, Zhiqi Shen, "An of recent algorithms in task allocation in Multi-agent Systems Efficient Task Allocation Protocol for P2P Multi-agent Systems," ispa, is discussed. In addition an algorithm for the Task Allocation pp.11-18, 2009 IEEE International Symposium on Parallel and Decision Maker (TADM) is proposed showing the scenario Distributed Processing with Applications, 2009.  Zaki Zaki Brahmi, Mohamed Mohsen Gammoudi, Malek Ghenima: of allocating tasks to specific agent/s. Finally, a preliminary Cooperative Agents Based-Decentralized and Scalable Complex Task experiment is then conducted, indicating that TADM has the Allocation Approach Pro Massive Multi-Agents System. ACIIDS (2) scalability advantage comparing to most recent systems. We 2010: 420-430 plan to propose new algorithm for the coordination  4.Cheng, J., andWellman, M. , “TheWALRAS algorithm: A convergent distributed implementation of general equilibrium outcomes,” mechanism selection in the decision support module. Also, Computational Economics, 12, 1–24 ,1998. we intend to propose a coordination module in our DMAIS  D. J. Watts and S. H. Strogatz. 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