Page 294 - Artificial Intelligence for the Internet of Everything

272 Artificial Intelligence for the Internet of Everything

Mihailescu, Spalazzese, and Davidsson (2017) introduce the idea of
emergent configurations (EC) for an IoT driven by user requirements.
The idea is to dynamically orchestrate heterogeneous “things” in a manner
that enables goal-directed behavior in support of a user’s requirements. In
this chapter we explore the idea of online agent creation and deployment
as a way to realize an EC. In the context of our work, meta-agents are agents
in a multi-agent software paradigm that utilizes reasoning to both construct
and deploy special-purpose agents that form an EC. Unfortunately, but
opportunistically research-wise, reasoning models that can support the idea
of meta-agents in the context of an EC have not been explored. To realize an
EC using meta-agents we need to first manage the complexity of the
problem, then develop a multi-agent framework capable of supporting
meta-agents, and, finally, explore reasoning models, such as belief-desire-
intention (BDI) modeling (Georgeff, Pell, Pollack, Tambe, & Wooldridge,
1999), for autonomous meta-agents.

14.2 MANAGING COMPLEXITY
Inhisseminalwork,TheSciencesoftheArtificial(Simon,1990;seealsoValckenars,
Brussell, & Holvet, 2008), Simon explores the laws that bound artificial
systems; in essence, he tries to develop a corollary to the laws of physics, but
applied to artificial systems. In this work Simon outlines three assumptions that,
when they hold true, require that any successful artificial system be holonic;
systems characterized by a pyramidal structure. Simon’s first assumption is that
of bounded rationality. Intelligent systems have a bounded, or limited, capacity
for computation and communication. As the limits of that capacity are
approached,addingmoreresources(fastercomputers,highbandwidthcommu-
nication) produces diminishing returns. The implication is that real-world intel-
ligent systems must be able to make decisions based on imperfect information
and limited computations. Simon’s second assumption is that intelligent systems
will operate in demanding environments. Intelligent systems must address
nontrivial problems, make effective use of the resources available to them,
and grow in complexity only in service of achieving a successful outcome. Sys-
tems that cannot do this will invariably fail and be replaced by more successful
systems. Simon’s final assumption is that intelligent systems must be able to oper-
ate in a dynamic environment. Operating in a highly dynamic environment
requires that systems have the capacity to quickly evolve to adapt to changing
contexts. This requirement has implications for the idea of optimality. In a
fast-changing environment the time required to design and develop a solution