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Active Inference in Multiagent Systems 73

a peer-to-peer manner with low computational complexity. While standard
BP does not guarantee convergence, it performs well in practice.
The free energy principle does not dictate the specifics of a generative
process, i.e., the structure/components of the generative density p(s,ojm)
required to define the free-energy function. Nor does it prescribe the algo-
rithms that need to be employed to minimize free energy. However, it pro-
vides a unifying framework that can be tailored to specific environments and
systems. Next, we apply the free-energy formalisms to the design of adaptive
multiagent teams, defining appropriate abstractions, and discussing their
implications for the IoE functional requirements.

4.3 APPLICATION OF ENERGY FORMALISM TO
MULTIAGENT TEAMS

4.3.1 Motivation
A team consisting of human and machine agents is a decentralized purpose-
driven system. One of the main challenges in defining adaptive team behav-
ior is in realizing global team-level perception and control, and correspond-
ing optimization processes, into local inferences and decisions produced by
individual agents without external control.
In our previous work we showed how free-energy minimization can be
applied to define adaptive behavior in teams that execute given multitask
missions (Levchuk, Pattipati, Fouse, & Serfaty, 2017). Examples of teams
include military organizations, manufacturing teams, and many other
project-based organizations. These teams interact with their environment
by jointly assigning and executing tasks; optimal teams have the highest task
execution accuracy and/or the fastest execution times. In the domain of
project-based teams, we considered team members (agents) to possess high
levels of intelligence, and thus assumed that agent-to-task assignment
decision-making processes should not be externally controlled. We defined
observations as the outcomes of task execution, and treated task assignments
as a world state variable hidden from the agent. Consequently, the percep-
tion phase estimated the probability of agent-to-task assignments, while the
control phase defined a team’s organization structure, including the roles and
relationships that constrain the tasks that agents could assign and execute,
specifying a formal process for the team to resist its disorder.
In this chapter we review an instantiation of energy-minimizing adaptive
behavior in a distributed decision-making setting. This entails a more gen-
eral setup than project-based teams, motivated by the following. First, we