The experiments are set within a society of 75 agents and the number of capability types within the context is set to 3 (referred to asα, β and γ).1 Each agent is assigned with a competence level of 1 for a certain capability type (either α, β, or γ) and 0 for the remaining two. Therefore, any given agent within the society has the ability to perform only one capability type. Within the society these capabilities are equally distributed with 25 agents having the ability to perform a certain capability type. All agents are assigned a single task spanning 50 time slots (as per equation 4.4 in Section 4.2.1.1). Each time slot contains a single action that requires a competence level of 1 (of the specified type) to achieve it. These capability types required are randomly distributed within a task. The initial rewards for the tasks are set according to a normal distribution (as per equation 4.5 in Section 4.2.1.1) with a mean£10,000 and a standard deviation of£2,500. Based on our initial experiments, themdf parameter for the penalty charge is set to 4 (as per equation 4.6 in Section 4.2.1.3).
In each experiment, the society differs in terms of its availability of resources. These are termed resource settings and are referred to asRSi wherei represents the number
1It is important to note that even though the reported results are for an agent community with 75
individuals, we have carried out these experiments in a broad range of settings where we have observed the same trends. Thus, although we present results for a specific instance here, the results are broadly indicative of what we have seen elsewhere.
Simulation Parameter Value Number of agents within the society 75
Types of capabilities α, β, γ
Initial task duration –Tinit 50
Initial Reward –Rinit µ=£10,000;σ =£2,500
mdf parameter 4
Resource settings RS1, RS2, . . . , RS25
TABLE5.1: Summary of the simulation parameters.
of other agents that each agent is aware of per capability. For example, at RS4 each
agent is aware of the existence of 4 other agents with capabilityα, 4 withβ and 4 with
γ. In the maximum resource setting (referred to as RS25), each agent knows about all
the other agents, hence it has maximum access to the resources within the system. On the other hand, in the most constrained resource setting (referred to asRS1), each agent
is only aware of the existence of a single (randomly selected) agent per capability. In between these two extremes, we define a series of 12 intermediate settings, where each agent is aware of the existence of 2, 4,. . ., 24 other agents per capability (referred to as
RS2,RS4etc.). Table 5.1 presents a summary of these simulation parameters.
It is important to point out that all three methods (argue, evade, and re-plan) used in this evaluation, tend towards the simpler end of their respective possibilities. However, our purpose here is not to exhaustively cover all forms of argumentation, evasion, or re- plan techniques. Rather we seek to evaluate the broad trade-offs involved in engaging in argumentation, thus, concentrating on the simpler models provides an initial point of departure. To this end, we disable the social influence model to prevent conflicts of opinions occurring within this context and concentrate mainly on the conflicts of interests that occur due to disparate motivations of the respective agents (refer to Sec- tion 4.1.2). However, in Chapter 6 where we explorehowagents may argue in a society, we enable both forms of conflicts and carry a more detailed in depth analysis of the different ways an agent may use argumentation to resolve these within a multi-agent context.
To evaluate the overall performance of the different strategies (specified in Section 5.1) in the experimental settings described above, we used the following metrics:2
• Effectiveness of the Strategy
We use thetotal accumulated penaltyincurred by all agents within the society as a measure of effectiveness. If this value is low, the strategy has been effective in
2These metrics are not novel to our work, both [Jung et al., 2001] and [Ramchurn et al., 2003] used
handling the conflicts that have arisen in the society. On the other hand, if the value is high, the strategy presents a less effective means of resolving conflicts.
• Efficiency of the Strategy
This reflects the computational cost of interaction incurred by the society, while using a particular strategy to resolve conflicts. We use thetotal number of mes- sagesexchanged between all agents within the society during the interaction as a metric to measure this effect. This provides a good metric because longer interac- tions, which tend to consume more resources from the agents, also take a higher number of messages to complete. On the other hand, shorter interactions, which tend to consume fewer resources, only incur a smaller number of messages. Thus, the number of messages exchanged has a strong correlation to the amount of re- sources used within the system. The total number of messages encapsulate the messages used to overcome conflicts and reach agreements (including reasons and alternatives exchanged as meta-information), and the messages associated with reneging from agreements. Thus, in this context, a strategy that involves fewer messages is said to have performed more efficiently than one that uses a higher number.
Having detailed the experimental setting, we now state our main observations, analyse them, and draw conclusions regarding their impact within a multi-agent society.