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76 Artificial Intelligence for the Internet of Everything

Agents

1 2 3 4

1 2 3 4 Relations:
-Superior-subordinate
-Lateral
(A) (B)
Fig. 4.3 Variables of team structure. (A) Decision decomposition. (B) Agent network.
superior agent combines the vectors of subordinates into a set of candidate
team-level decision vectors, evaluates them against a team’s objective func-
tion, and communicates the best vector to the subordinates for implemen-
tation. The number of decision vector values the agents can evaluate during
a specified time period is constrained by internal capacity. Lateral relation-
ships can also be defined among the agents, allowing one agent to inform
another about their local decisions vectors.
The decision-making process (Rivkin & Siggelkow, 2003) presents a
heuristic solution to a distributed optimization problem, but provides no
guarantees of convergence or efficacy even with the introduction of global
incentives for agents. This heuristic also does not explain the causes of an
underlying behavior, nor prescribes an adaptive behavior for the team or
its members. We can still evaluate and compare the behaviors of teams of
agents using these heuristics in terms of the time it takes to produce their
solution, its proximity to the true maximum, and the amount of exploration
versus exploitation in the state space of all decision vectors the agents jointly
generate. In the following, we offer a formal optimization process that solves
this problem in a distributed manner using free energy–minimization prin-
ciples, along with the concomitant adaptive team behaviors exhibited by the
team members during a search process. We show that an energy-based
search provides a mechanism to adapt the multiagent behaviors and signif-
icantly outperforms the discrete optimization heuristics by Rivkin and Sig-
gelkow, described in the previous section.

4.3.3 Distributed Collaborative Search Via Free Energy
Minimization
We recast the problem described above as a joint inference over a factorized
objective function in Section 4.2.4. In the distributed search problem of