Preface

    The continuous international efforts to enable everyday devices to partici-
pate in the emerging Internet of Things (IoT) ecosystem has led to an explosive
increase in the number of smart devices that surround us. However, our capacity
as humans to meaningfully process, manage, control, and interact with them is
limited by human nature, our interests and our technical fluency. The coming
new digital market envisions an ambient environment where the physical world,
computer-based systems and humans converge and seamlessly interoperate, re-
sulting in an improved social and economic marketplace.
    Collectively, the public sector, industry, academia, end-users, SMEs, and
large corporations constantly feed the, already, high expectations of IoT. Artifi-
cial Intelligence (AI) has the capacity to facilitate the anticipated socio-economic
transformation caused by the proliferation of IoT through innovative algorithms
and techniques.
    The Artificial Intelligence and Internet of Things (AI-IoT) series of work-
shops aims at providing the ground for disseminating new and interesting ideas
on how AI can make valuable contribution in solving problems that the IoT
ecosystem faces. The virtualization of devices and smart systems, the discover-
ability and composition of services, the interoperability of services, the distribu-
tion of resources, the management and event recognition of big stream data, and
the development of algorithms for edge and predictive analytics are only a few
of the problems that look for intelligent human-centric solutions that could find
application in smart cities, smart farming, transportation, health, smart grid,
tourism, etc.
    The second installment of the workshop – 2nd AI-IoT 2016 – was co-located
with ECAI 2016 in The Hague, Netherlands and featured a keynote by Prof. Dirk
Helbing from ETH Zurich, Switzerland, entitled “Towards Smarter Societies”
and five accepted papers, resulting in an intriguing technical program. Papers
accepted in the workshop gave special emphasis in AI-related topics such as:

 – Machine learning
 – AI planning
 – Reasoning under uncertainty
 – Personalization
 – Classification
 – Real-time event recognition
 – Multi-agent systems

that have been explored in smart societies, tele-assistance, smart tourism, em-
bedded sensor fusion, for activity recognition in surveillance and security sys-
tems, and for detecting treads and abnormal activities in maritime surveillance.
    Specifically in this proceedings the contributions of the accepted papers are as
follows. In the “Third Generation Teleassistance: Intelligent Monitoring Makes
the Difference”, Rafael-Palou et al. propose an intelligent monitoring solution
for elderly people, integrated in an IoT-based tele-assistance system, demon-
strating how it contributes in offering enhanced support to both end-users and
caregivers. Machine learning methods based on SVM are used for detecting in-
teresting events and issuing alarms in case of an emergency. Results from deploy-
ing the system in real-life situations are presented. Marzal et al. in “Temporal
Goal Reasoning for Predictive Performance of a Tourist Application”, discuss
a goal reasoning framework that identifies if the context information acquired
from several external resources dictates a change in the execution of a temporal
plan. TempLM, a temporal planner that uses temporal landmarks for planning
with temporal deadlines, detects situations of future failures and opportunities
in the plan execution. The capability of the planner to adapt to external events
is showcased in a smart tourism scenario. Babli et al. in their paper entitled
“An Intelligent System for Smart Tourism Simulation in a Dynamic Environ-
ment” present an AI planning-based system for the smart tourism domain, where
the goal is to construct a personalized tourist agenda of places a tourist could
visit according to his preferences. The system not only creates the agenda, but
also monitors its execution in real-time through simulation. Emphasis is given
in dynamically reacting to changes in the environment by adapting, if neces-
sary, the tourist agenda, through reformulation of the planning problem, to re-
flect the new state of the environment in real-time. In “Extending Naive Bayes
with Precision-tunable Feature Variables for Resource-efficient Sensor Fusion”,
Galindez Olascoaga et al. focus on the tradeoff between resource efficiency and
inference accuracy, by tuning feature quality in sensing devices. An extension to
the naive Bayes classifier is implemented and evaluated in sensor fusion tasks.
The algorithm is capable of dynamically tuning feature precision as a function
of the incoming data quality, the difficulty of the task and the resource avail-
ability. In the last paper, “A Distributed Event Calculus for Event Recognition”,
Mavrommatis et al. present a distributed approach for stream reasoning, called
dRTEC, based on a dialect of event calculus. dRTEC employs the Apache Spark
framework to perform scalable event recognition and detect significant patterns.
    The organizers would like to thank the authors for submitting their work
to the workshop, the members of the program committee for their valuable
contribution in reviewing the papers and, of course, the numerous participants
of the workshop.


                                               Constantine D. Spyropoulos
                                                          Georgios Pierris
                                                        Grigorios Tzortzis

                                            Organizers of 2nd AI-IoT 2016

             Workshop site: http://2nd-ai-iot2016.iit.demokritos.gr/