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80 Artificial Intelligence for the Internet of Everything

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Team
Factor
Requirements
structure @
graph
network
time t +1
Team structure @ time t
Fig. 4.5 A notional team structure adaptation workflow.
of internal and external workloads on a team’s perception and action pro-
cesses using delay models and/or message transmission errors (the classic
“delay-accuracy” tradeoff in team performance). Schematically, the team
structuring problem can be decomposed in two steps (Fig. 4.5). First, we
aggregate the decision and factor nodes to balance the internal and external
workloads via cluster analysis by minimizing the inter-cluster links, subject
to constraints on the workload capacities of agents. Second, we use this net-
work as a set of requirements, and map it to the current agent network, re-
aligning agent network parameters (i.e., capacities) as the factor graph
evolves (e.g., changes its decision reward parameters).

4.4 VALIDATION EXPERIMENTS
4.4.1 Experiment Setup

We conducted several studies to validate our proposed adaptive team behav-
ior model. First, we generated a collection of synthetic distributed decision-
making problems by randomly generating the values for the joint reward
function. We manipulated the density of dependencies between variables
using a parameter K defined as the average number of variables influencing
a factor node. We computed several assessment metrics, including the per-
cent of trials where a team converged to an optimal solution (determined as
producing a vector with a reward value within a small threshold of the opti-
mal value), and a normalized payoff (computed as the fraction of the discov-
ered decision payoff compared to the optimal one). We evaluated team
performance over time, as well as using a time-averaged payoff. Second,
we introduced random periodic variations of the parameters in the joint
reward function to evaluate how quickly the teams can recover from these
changes. The latter analysis enables us to quantify the attributes of resilience
in a team: (1) capacity to absorb changes, (2) recoverability, and (3) adaptive
capacity (Francis & Bekera, 2014).