

![0_image_0.png](0_image_0.png)

From this model we distinguish two different types of semantic features: a player type and a territory type. For example (Quebec, player, green, nbArmies, 4, time, 4) is a territory semantic feature that means Quebec territory is owned by the green player and has four armies. However, (blue, nbTerritories, 4, time, 1) is a player semantic feature that signifies a blue player has four territories at step 1.

The first extracted semantic features of the initial state of the game cause the creation of factual agents. For example, a semantic feature as (red, nbTerritories, 0, time, 1) will cause the creation of red player factual agent.

During the game progression, the entry of a new semantic feature **to the**
system may affect some agents state. A factual agent of type (Alaska, player, red, nbArmies, 3, time, 10) become (Alaska, player, red, nbArmies, -2, time, 49) with the entry of the semantic feature (Alaska, player, red, nbArmies, 1, time, 49). Alaska agent sends messages containing its semantic feature **to all the other** factual agents to inform them about its change. The other agents compare their own information with the received one. If an agent is interested by this message (the proximity measure between the two semantic features is not null) it updates its semantic feature accordingly. If the red player owned GB before the semantic feature (GB, player, blue, nbArmies, 5, time, 52), both red player **and blue player** will receive messages because of the change of the territory owner.

If we take again the preceding example (Alaska territory), Alaska agent computes its new PP (valProximity). The computation of valProximity in our case is given by: number of armies (t) - number of armies (t-1) e.g. here valProximity = 1-3 = -2. PS and PA are deduced thereafter from PP. The agent verify then the predicates of its current state outgoing transitions in order to change state.

To pass from Deliberation state to Decision **state for example the PS must be** strictly positive. During this transition, the agent will send a **SupportMessage** to a friend and an AgressionMessage **to an enemy.**

## 6 Conclusion

The paper has presented a decision support system which aims to help decisionmakers to analyse and evaluate a current situation. The core of the system rests on an agent-oriented multilayer architecture. We have described here the first layer which aims to provide a dynamic information representation of the current