Coalition Formation through Motivation and Trust

1. Coalition FormationthroughMotivation and Trust Nathan Griffiths Michael Luck

2. Introduction • The paper is concerned with building a model of cooperation that: • Avoids missed opportunities for cooperation • Avoids the redundant computation in re-constructing similar groups • Accounts for the motivational side of cooperation • The authors take a medium-term view of cooperation and talk about a medium-term coalition known as a clan

3. Introduction – General Information • Cooperation is the fundamental underpinning of multi-agent systems. Each agent has differences in: • Capabilities • Knowledge • Resources • Agents are naturally self-interested. They will not cooperate unless they perceive some advantage. • The perception of an advantage comes as the result of motivation and trust.

4. Introduction – Models of Cooperation • The authors identify 2 broad groups: • Teamwork • Coalition Formation • Teamwork is task based. It is concerned with achieving a specific short-term task. • Coalition establishes a group of agents with a common goal. Coalitions tend to be long-term relationships when compared with teams.

5. Introduction – Models of Cooperation • The biggest limitation of teams is that an agent won’t join a team that doesn’t share it’s immediate goals, even if the agent’s goals align with the groups long-term goals. The authors refer to this as ‘missed opportunities’ and bring it up several times throughout the remainder of the paper. • Teams must be reformed for every task even if it includes the same set of agents. There is a cost associated with forming and dissolving teams, especially when dealing with a large number of agents.

6. Introduction – Models of Cooperation • The technique of congregating is discussed where agents search for cooperation within the congregation rather than the entire population. • The concept of BDI for agents is discussed, where: • B = Beliefs the agent has about itself and others within the environment. • D = Desires representing the states the agent wants to achieve. • I = Intentions representing the plans the agent adopts in pursuit of the desires. • When deciding if to cooperate with other agents, the following apply: • Motivation determines if an agent wants to cooperate. • Intention determines if they can cooperate. • Trust determines the perceived risk of cooperation.

7. Motivation • Motivation can be equated to an agent’s high level desires. It guides all aspects of the agents behavior. Different agents have different desires. • Each motivation is represented by (m, I, t, fi, fg, fm) where: • m = The name of the motivation • i = The current intensity • t = The threshold • fi = The intensity update function • fg = The goal generation function • fm = The mitigation function

8. Motivation • When the intensity (i) of a motivation (m) exceeds its threshold (t), fg is used to generate a set of goals. The agent works on the goal that provides the most motivation. When the goal is achieved, the mitigation function (fm) is used to reduce the intensity based on the motivational value of achieving the goal.

9. Trust • Trust is the agents estimate of how likely it is that another agent will fulfill it’s commitment in a cooperative effort. • A trust value of another agent can be built based on experience over time with that agent. This value is represented with a number between 0 and 1 where: • 0 represents complete distrust • 1 represents complete, blind trust • Trust can be broken into general and situational trust where: • General trust is an overall view based on all previous interactions • Situational trust is based on interactions in similar situations, meaning similar motivational values

10. Stages of Cooperation • The stages of a plan are described as: • Plan selection – an agent selects a plan that will achieve it’s highest intensity motivation. • If the plan requires cooperation, the agent iterates through the steps of the plan and determines which agents will be needed based on their capabilities and trust worthiness. • Requests are made to the required agents who check their own motivations and intentions to decide if to participate. • If enough agents agree, the plan can be executed successfully. • If enough agents don’t agree, the plan is deemed a failure. • Regardless of failure or success, all agents involved update the information they store about others to aid in future decisions.

11. The concept of Clans • As previously discussed with teams, short-term coalitions are inefficient because they spend excessive resources forming and dissolving. • Long-term coalitions don’t work because the motivations of agents within the coalition change gradually over time and the coalition becomes less beneficial to those involved. • A medium-term relationship seems to be the most stable. This is the most likely scenario for the formulation of a successful clan. • Key elements in the formation of a clan are motivation and trust.

12. Reasons to form a Clan • Avoid missed opportunity as discussed earlier. • Reduce the scale of the number of agents we deal with, thus reducing the search space and communication costs. With a clan we also reduce the frequency with which these searches must be done. • An agent should only join a clan with other agents that are trusted. The flip side of this is that early on an agent doesn’t have a high enough trust factor with any other agents to justify joining any clan. • When looking for agents to execute a plan, these agents are more likely to agree to cooperate even if they have no immediate motivation. Membership in the clan gives them a higher motivation to cooperate. This relationship can be referred to as kinship. • If an agent doesn’t know of any other agent with some desired capability it can request information from other clan members.

13. Miscellaneous and Conclusion • Pseudo code is given for forming a clan and determining if the motivation is high enough to join a clan. A more detailed examination of the pseudo code can be done during the in-class critique. • A model is described that was used to test whether an agent has more success as part of a clan or not. The conclusions indicate that an agent will have a higher chance of success as part of a clan. Please note when looking at Figure 6 that the left hand graph represents failed interactions, while the right hand graph represents successful interactions.