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and events, and the mechanisms to resemble human-like common-sense knowledge and reasoningcapabilities.From the cognitive perspective, the problem can be addressed as an understanding problem. Comprehendinga situation that takes place in a context might involve, for example, the inference of implicit,non-deterministic or delayed effects. A delayed effect of turning on a tap in a kitchen sinkwhose plug is in place, will be a water overflow. From a behavioral perspective, the problem can beaddressed as a planning problem of deciding what action to take in certain given circumstances. Acommon-sense strategy for planning and understanding, such as that presented in [150] would, therefore,appear to be the most compelling approach towards emulating the human-like rationality andreasoning capability.Finally, the last challenge that should be faced when building systems for Ambient Intelligenceis that of addressing novelty. It has to be noted that unexpected situations, which in words of DougLenat et al. [80] suppose the bottleneck of intelligent systems, are the most common situations foundin Ambient Intelligence contexts. The way in which people react to these unexpected situationsprovides an idea of the direction in which efforts should be addressed. Generally, when facing newsituations, people tend to establish some similarities with past experiences, or resort to their generalknowledge of how things work, the so-called common-sense knowledge, or even look for advicein books. Whatever the case may be, it is axiomatic for this thesis that only Ambient Intelligencesystems will be sufficiently flexible to support the scenarios envisioned in [35] when common-sensereasoning starts being considered as a structural part of such systems. In this sense, the associatedworking hypothesis is that understanding and modeling common-sense reasoning, in such a way thatit can be automatically performed, is a key challenge that, once achieved, would allow systems forAmbient Intelligence to be indeed intelligent.At this point, the main axioms and working hypotheses of this work have been roughly outlined,providing an overall view of the main challenges faced when building systems for Ambient Intelligence.The following sections will address with higher levels of detail, the fundamental problemsthat have motivated this work. The third section will present the aims and objectives of this thesis.While the main contributions are presented in section four. Finally, the last section presents a briefdescription of the content addressed in each of the consecutive chapters that follow this chapter.1.2 MotivationThe previous section has pointed out some of the main challenges that need to be addressed whenbuilding systems for Ambient Intelligence. These challenges are also considered to be part of themain concerns that have motivated this thesis. However, for the sake of precision, this section simplysynthesizes these general aspects by providing a list of intentions, which will be addressed with moredetail in the following chapters. These intentions establish the directions towards which efforts needto be directed in order to achieve the ultimate goal of building systems for Ambient Intelligence.In this regard, the main peculiarity which distinguishes Ambient Intelligence from other fields ofknowledge is the interdisciplinarity of the challenges faced. This interdisciplinarity therefore involvesspecialization in a wide variety of technologies, thus turning the task of building comprehensiveapproaches to Ambient Intelligence into a very complex endeavor that requires knowledge of a widerange of technologies.Augusto et al. enumerate in [8] some of the main technologies that are encompassed in AmbientIntelligence. As stated by these authors, building systems for Ambient Intelligence is a task thatrequires a certain level of expertise in fields such as Sensor Networks, Artificial Intelligence, or Multi-Agent Systems among others. The adoption of an approach based on a single technology does not8
suffice to tackle the endeavor of enhancing Ambient Intelligence systems with capabilities to behavein a self-managed, pro-active, dynamic and goal-driven fashion. On the contrary, a comprehensiveapproach is demanded by the need to address the different problems that arise in this regard. However,the highly demanding task of devising such a comprehensive solution cannot always be afforded bysmall research entities. As will be described in the next chapter, the main contributions to this fieldcome from big research consortia or from European funded projects. This situation poses the need tomake assumptions or to overlook relevant aspects when trying to make a contribution in this field.It is therefore futile for this thesis to attempt to face all these challenges from scratch, although atthe same time, this thesis is indeed engaged in minimizing the number of assumptions required. Thisendeavor can be feasibly undertaken since this thesis is encompassed in the comprehensive approachcarried out by the ARCO Research Group, at the University of Castilla-La Mancha. Under the directionof this Research Group, a divide-and-conquer approach has been adopted towards AmbientIntelligence. It has been necessary to identify the different disciplines that include the challenges that,to date, remain unsolved, and address them individually. As a result, this thesis rather than having todeal with all these challenges from scratch, can focus on a specific set of them, and ground the proposedapproach in the research and technological solutions provided by previous projects and thesesdeveloped within the group that have contributed to the state of the art.The multidisciplinary fields in which the ARCO Research Group has specialized entitles it toundertake a comprehensive approach to building Ambient Intelligence systems, in which three mainlayers can be identified. The inner layer deals with technological issues, the second layer abstractsthe communication aspects, and finally, the third layer focuses on orchestrating the underlying layers.This third layer is responsible for enabling the overall system to resemble cognitive and behavioralhuman-like capabilities.In this respect, the first two layers correspond with two theses completed within the activities ofthe research group. The first layer has been devoted to managing the different technologies that mightappear in an Ambient Intelligence environment. In this regard, the lack of standards homogenizingthe access to the deployed devices, demands some additional mechanisms that automate functionssuch as the device and service discovery. It is also necessary to apply some adaption mechanismsin order to conceal the technological details to the upper layers, providing a standardized manner forusing the services provided. These challenges have been addressed in the thesis work of Dr. D. Villa[144], which has therefore contributed with an effective solution to address the device dynamism andheterogeneity problem that characterize Ambient Intelligence environments. The second layer dealswith the communication issues, in seeking to reproduce a Place & Play philosophy, that serves tooverlook the technology-dependent details. These challenges have been addressed in the thesis of Dr.F. J. Villanueva [146] that has contributed to the state of the art with an architectural approach to caterfor the Ambient Intelligence deployment requirements.The present thesis is framed in the results and conclusions drawn from these previous works. Inthis respect, the contributions made by the aforementioned thesis lay the foundations for the integrationand communication of sensor and actuator networks, proposing an efficient solution to addressthe dynamism and heterogeneity that characterize such contexts. Nevertheless, the Ambient Intelligenceenvironment is not yet capable of autonomously understanding and reacting the unforeseensituations, and that is the gap that this thesis has intended to fill.Traditionally, Ambient Intelligence solutions have been mainly concerned with anticipating useractions and needs. For example, resorting to the first scenarios described in [35], the followingquestion is therefore stated: What would be the system response if, on her arrival, Maria twistsher ankle. Would the Ambient Intelligence system be capable of recognizing the possible injury in9
Page 1: DEPARTAMENTO DE TECNOLOGÍAS Y SIST
Page 4 and 5: María José Santofimia RomeroTelé
Page 7: ResumenLa Inteligencia Ambiental, p
Page 11 and 12: ContentsContentsList of TablesList
Page 13: CONTENTSVII7.4.4 The Plan Executor
Page 17: List of Figures4.1 Kripke model for
Page 21: Part IPreliminaries3
Page 24 and 25: This gap poses an urgent need to de
Page 28 and 29: Maria’s ankle as an impediment to
Page 30 and 31: 1.3 Aims and objectivesGiven that t
Page 32 and 33: The fourth item concerns the way th
Page 34 and 35: of such goals and desires and how t
Page 36 and 37: tion, is addressed by coping with a
Page 38 and 39: networking from being widely spread
Page 40 and 41: The MERL’s Ambient Intelligence f
Page 42 and 43: tions come into play. These scenari
Page 44 and 45: interact with electronic devices, e
Page 46 and 47: tionally, the middleware architectu
Page 48 and 49: The idea behind the work proposed h
Page 50 and 51: esources under a middleware based o
Page 53: Part IIUnderstanding35
Page 56 and 57: obvious. In this sense, sociologist
Page 58 and 59: true throughout a time interval, or
Page 60 and 61: application domain is (true, false)
Page 62 and 63: (SubAsbstrac Nathan Nathan2007)(Sub
Page 64 and 65: Moreover, events not only cannot be
Page 66 and 67: epresented by means of the notion o
Page 68 and 69: Context-awareness is one of the mai
Page 70 and 71: In any case, the adopted behavioral
Page 72 and 73: when the water level starts increas
Page 74 and 75: Traditionally, these responses have
Page 76 and 77:
true.Additionally, the meaning of t
Page 78 and 79:
(6) Statement → Service-ID perfor
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the set of possible contexts, and M
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in which S is a nonempty set of sta
Page 84 and 85:
are considered possible given the p
Page 86 and 87:
cope with the demands involved in d
Page 89 and 90:
Chapter 5Understanding Context Situ
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Figure 5.1: Overall view of the pro
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adjusting existing knowledge to sim
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of some events involves the stateme
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( new-statement { picker } {is loca
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The way of determining which after
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CL-USER > ( the-x-of-y-is-z { enter
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CL-USER > ( the-only-x-of-y-is-z {
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CL-USER > ( get-element-fluent ( lo
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The specificity of the propositiona
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Following the same dynamic, the dif
Page 111:
part is intended to propose a solut
Page 115 and 116:
Chapter 6Behavioral Response Genera
Page 117 and 118:
and action selection by means of a
Page 119 and 120:
wants the room to be at a higher te
Page 121 and 122:
state of the world with those plann
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conflict. The later strategy requir
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Function f returns the actions, fro
Page 127:
next step selected in the plan. The
Page 130 and 131:
of it. It is also possible to try t
Page 132 and 133:
Agent System (MAS), individual agen
Page 134 and 135:
the requirements stated for the BRG
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The action planning algorithmMaking
Page 138 and 139:
The advantages underlying service c
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effects. On the contrary, an approp
Page 143:
Part IVValidation and discussions12
Page 146 and 147:
taking place. The interpretation of
Page 148 and 149:
The key elements of the evaluation
Page 150 and 151:
also been proved to serve as a mean
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Table 8.2: Simulation Configuration
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the knowledge-base, it saves time i
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effects and the sensed ones leads t
Page 158 and 159:
Table 8.3: Personal information of
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Scenario Interpretations Number of
Page 162 and 163:
understand the terms used to descri
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Finally, the causal explanation app
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2. A2: To provide a service composi
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System has to be motivated by goals
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een addressed by this thesis. Howev
Page 173 and 174:
Bibliography[1] Gregory D. Abowd, A
Page 175 and 176:
[25] Diane J. Cook, Juan C. Augusto
Page 177 and 178:
[54] Tao Gu, Hung Keng Pung, and Da
Page 179 and 180:
[83] Clemens Lombriser, Nagendra B.
Page 181 and 182:
[110] Davy Preuveneers, Jan Van den
Page 183 and 184:
[134] John F. Sowa. Conceptual Stru
Page 185:
Part VIAppendix167
Page 188 and 189:
Ambient Intelligence environment, i
Page 190 and 191:
invocation. However, in reality the
Page 192 and 193:
consists in querying the Topic Mana
Page 194 and 195:
Figure A.4: Multi-Agent System over
Page 196 and 197:
The result of the planning algorith
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concepts and relationships are impl
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As listed below, the recognition ac
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184Figure A.8: Sequence diagram for
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}query = " ( b−wire ( car ( list
Page 207:
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