1. Agent coordination Using EJBs and XML WSs in Battlefield Environment
Bijan Zamanian and Zeki Bayram
Eastern Mediterranean University
Department of Computer Engineering

2. Outline Introduction What is an agent? What is coordination?
Why coordination
Coordination models
Our model
Proposed architecture
Demonstration of proposed architecture
Conclusion

3. introduction
Complex tasks are often carried out by teams consisting of individuals, because no one individual has the collective expertise, information, or resources required for the effective completion or performance of a task. Agents can cooperate to facilitate achieving a common, complicated and large scale goal using some of their characteristic like Intelligence , autonomy. In such a case each agent is responsible for a part of the goal . But to ensure a community of individual agents acts in a coherent manner they need coordination. Coordination may require cooperation, but cooperation among a set of agents does not necessarily results in coordination.

4. What is an agent?
A software agent is a piece of software that acts on behalf of a user or other programs.
An agent is defined in terms of its behavior.
Agents commonly include the following concepts
persistence (code is not executed on demand but runs continuously and decides for itself when it should perform some activity)
autonomy (agents have capabilities of task selection, prioritization, goal-directed behavior, decision-making without human intervention)
sociability (agents are able to engage other components through some sort of communication and coordination, they may collaborate on a task)
reactivity (agents perceive the context in which they operate and react to it appropriately).
Intelligence (agents are capable of reasoning and learning )

5. What is an agent? (cont 1.)
A cooperative system of agents may fall into one or more of these categories :
distributed agents (being executed on physically distinct computers),
multi-agent systems (distributed agents that do not have the capabilities to achieve an objective alone and thus must communicate),
mobile agents (agents that can relocate their execution onto different processors).

6. What is coordination? Coordination in general : Agent coordination.
Act of making different people or things work together for a goal or effect.
Agent coordination.
The process by which agents reason about their local actions and the (anticipated)
actions of others .

7. Why coordination? Preventing anarchy
Coordination is necessary or desirable because in decentralized
agent-based systems, anarchy can set in easily.
Dependencies between agents' actions
Actions of multiple agents need to be coordinated because of
dependencies between agents' actions.
Insufficiency of local views
Agents only have local views, goals and knowledge which may
conflict with others or take improper action by the agent.

8. Coordination models Coordination model: Coordination architecture:
An agent coordination model is a conceptual framework .
Coordination architecture:
A coordination architecture is a software infrastructure.
Some well known coordination models are:
Direct, Meeting oriented,
Blackboard-based
Linda-like

9. Coordination models (cont 1.)
Direct
In Direct coordination models, agents usually coordinate using RPC-like primitives
or synchronous message passing.
Meeting oriented
In Meeting oriented models, agents coordinate using implicit or known meeting
points.
Blackboard-based
In Blackboard-based models, agents coordinate via shared data spaces to store
and retrieve information under the form of messages .
Linda-like
In Linda-like models, agents coordinate through tuple spaces which allow for
Insertion of tuples and retrieval of tuples using associative pattern matching.

10. Concerns about coordination models
Dynamics
Coordination strategies that handle highly dynamic task environments may not
keep up with all environment
Agent population properties
A coordinator (specially centralized ) can quickly degrade and becomes incapable
of processing the interactions if population of agents increases.
Quantity of interaction
If each agent interacts with every other agent, the number of paired interactions
will grow quadratically

11. Our model
Our model can be seen as an instance of blackboard-based coordination.
Our model obviates some coordination concerns (Dynamics, Agent population properties, Quantity of interaction)
Hierarchical control of agents
Actual reality vs. perceived reality (agents and coordinator may not have full or perfect view of reality)
Web services: eyes, ears of agents. Also effectors of agents. Has full view of reality, but reveals only part.
Coordinator: one for each group of agents

12. Our model (cont 2.) Web service Large scale goal ,policy
Carrying out missions
Coordination , mission assignment
Strategy ,planning
Large scale goal ,policy
Coordinator 1
Coordinator2
Coordinator4
Agent 1
Agent 3
Coordinator5
Agent 4
Agent 5
Coordinator3
Coordinator6
Coordinator
Agent 6
Agent 7
Web service

13. Proposed architecture
Our architecture is made of four main parts: Coordinator, Agents, Environment and Web service.
Web Service
EJB Coordinator
perceive /effect/ query
environment
send /receive task
send/ receive task result
Environment

14. Agent Architecture
Agents implemented as Java objects running in their own thread
mid
Miss.1
type
Askmission()
Miss.2
DoMission()
Agent
ammo
runner
RI_lookup()
health
Observe()
x,y,z
IsAccomplished()
Go()
Run()

15. How agents operate Mission request from superior (coordinator)
Web services used to be informed about environment, and cause changes to environment
No direct inter-agent communication
Report result of mission to coordinator
Built-in intelligence: observation and decision making

16. Coordinator Architecture

17. COORDINATORS Coordinators implemented as EJB’s (Enterprise Java Beans
Responsible for coordinating a group of agents
Agents in the field communicate with them to get initial mission,and to report result of mission
A different set of coordinators for each “team” possible in case of multi-part simulation

18. How THE coordinator works
Logic of mission creation
Coordinator generates missions for units its under command based on the units’ types and enemy types
How it perceives
Initially the coordination does not know about the presence of the enemy units. Initial perception through agent observations

19. Web services – MAINTAINER and revealer of reality
Data Source
AgendDb
Method 1
Method 2
Method n
Ws-Method 1
Ws-Method 2
Ws-Method n

20. WEB services Web services maintain the actual reality
Agents call methods to inquire about their environment (who is around me?)
Agents call methods to change environment (shoot the guy)
Web service methods reveal only part of the reality, given the situation of the requesting agent. Actions are carried out in a probabilistic manner

21. Properties of Our Architecture
Agents can exist in different java virtual machines or even on different physical machines.
Coordinators can be distributed on different machines
Job of coordination made easy through coordination hierarchy.
One simulation can run on multiple machines and share the same “environment” through using Web services. Different “teams” (e.g. enemies) can run in different machines, but participate in the same simulation
Our architecture is both extendable and scalable

22. Application of model:Battlefield simulation
Coordinator
Ground Fource
Troupers
Armored Units
Air Force
Fighters
A-VAX
Navy
Battleships
Submarines
Modeled armed forces:
WS- Coordinator

23. Conclusion Described the agent coordination problem
Defined an agent coordination architecture based on EJB’s and WS’s
Demonstrated the architecture in a battlefield simulation
Our architecture has nice properties: scalable, extendable

24. Demonstration of architecture in B.F.E
Web Service
EJB Coordinator
Environment

25. References
Dieter Fensel;Holger Lausen;Jos de Bruijn;Michael Stollberg;Dumitru Roman (2007):"Enabling Semantic Web Services". The Web Service Modeling Ontology. Springer Berlin Heidelberg Publishing. ISBN (Print) (Online).
H S Nwana, L Lee and N R Jennings, (1996): “Co-ordination in software agent systems.” BT Technol J Vol 14 No 4 October 1996.
Stollberg, M.; Haller, A.(2005): "Services Computing", 2005 IEEE International Conference on Volume 2,  July 2005 Page(s):xv vol.2
Stollberg, M.; Haller, A. (2005):" Semantic Web services tutorial", Mar-Apr 2001Volume: 2, Issue: 2 On page(s): On page(s): xv vol.2 ,Number of Pages: 2 vol. (xxi+660) ,ISBN: INSPEC Accession Number: ,,Date Published in Issue: :54:29
Weiming Shen; Hamada Ghenniwa; Yinsheng Li (2006): "Agent-Based Service-Oriented Computing and Applications", Pervasive Computing and Applications, st International Symposium on.3-5 Aug Page(s):8 – 9.

26. References (cont 1.)
Noh-sam Park; Gil-haeng Lee (2003):"Agent-based Web services middleware", Global Telecommunications Conference, GLOBECOM '03. IEEE Volume 6,  1-5 Dec Page(s): vol.6
Zhi-Zhong Sun; Bin Li; Liang Li (2007):"An Adaptive Agent Coordination Framework for Web Services Composition", Machine Learning and Cybernetics, 2007 International Conference on Volume 7,  Aug Page(s):
R. Scott Cost, Yannis K Labrou,Tim Finin:"Agent Communication Languages and Agent Coordination", Coordination of Internet Agents: Models, Technologies and Applications. July 01, 2000.
Willmott, S.; Pena, F.O.F.; Merida-Campos, C.; Constantinescu, I.; Dale, J.; Cabanillas, D. (2005): "Adapting agent communication languages for semantic Web service inter-communication", Web Intelligence, Proceedings. The 2005 IEEE/WIC/ACM International Conference on Sept Page(s):405 – 408