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84 Artificial Intelligence for the Internet of Everything

4.5 CONCLUSIONS

In this chapter we studied the problem of generating adaptive behaviors for
cooperating agents. We presented an application of the free energy–minimi-
zation principle to generate decentralized purpose-driven teams of agents.
Experiments with synthetic data establish that energy-based behavior results
in a higher performance on a distributed search task compared to discrete
decision-making heuristics. The minimum free-energy formalism provides
a mathematically sound mechanism for coupling perception and action
selection processes. Finally, the decisions to affect the environment through
actions, adapt by modifying perception, and adjust the architecture of a team
in terms of organizational structure among the agents can all be executed in a
distributed collaborative manner without the need for external controlling
agents.
One of the key innovations of our work is that it prescribes two general
interfaces that the intelligent adaptive agents must possess: generating, com-
municating, and incorporating experience and influence messages. Neither of
these interfaces alone can allow multiple agents to achieve the required
team-optimal decisions using distributed local computations. Our current
work is focused on defining a precise free-energy function that the encodes
the effects of team structure on decisions and communications, studying the
convergence properties of distributed perception and control processes,
obtaining the collaborative adaptation mechanisms for project-based teams,
and deriving high-level corollaries with general trends from lower-level free
energy–minimizing processes.

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