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                                ali_eldesouky@yahoo.com                            aisaleh@mans.edu.eg

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) [1] 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 [2] 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 [2], 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



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                                                                                                     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 [2], 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




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                                                                                                                                  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 [8], 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 [9]
        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. [10] 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 [11]
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 [12] 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 [13]. The authors in [14]
agent to another according to many parameters (e.g. number
                                                                              and [15] 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 [16] 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. [17] 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. [18]
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. [19] 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



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                                                                                                          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. [20] 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 [21] 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 [18] and with
                                                                    the Greedy Distributed Allocation Protocol (GDAP) [19]. 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




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                                                                                                        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 [22]. 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




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                                                                                                            ISSN 1947-5500
                                                                      (IJCSIS) International Journal of Computer Science and Information Security,
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not only on neighbours of the agent, but also other agents if                   [5]    Song Qiang, Lingxia Liu, "The Implement of Blackboard-Based Multi-
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