2,068 research outputs found
Contextual conflict determination among sensory events using cooperating agents
A cooperating-agents based algorithm is described to detect temporal consistency among sensory events and for constraint processing. We describe the algorithm using an example. Also we describe the cooperative aspects of the agent -based algorithm using an UML activity diagram
Regularity in speech rhythm as a social coalition signal
First published: 01 August 2019Regular rhythm facilitates audiomotor entrainment and synchronization in motor behavior and vocalizations
between individuals. As rhythm entrainment between interacting agents is correlated with higher levels of cooperation
and prosocial affiliative behavior, humans can potentiallymap regular speech rhythmonto higher cooperation
and friendliness between interacting individuals.We tested this hypothesis at two rhythmic levels: pulse (recurrent
acoustic events) and meter (hierarchical structuring of pulses based on their relative salience).We asked the listeners
to make judgments of the hostile or collaborative attitude of two interacting agents who exhibit either regular
or irregular pulse (Experiment 1) or meter (Experiment 2). The results confirmed a link between the perception of
social affiliation and rhythmicity: evenly distributed pulses (vowel onsets) and consistent grouping of pulses into
recurrent hierarchical patterns are more likely to be perceived as cooperation signals. People are more sensitive to
regularity at the level of pulse than at the level of meter, and they are more confident when they associate cooperation
with isochrony in pulse. The evolutionary origin of this faculty is possibly the need to transmit and perceive
coalition information in social groups of human ancestors. We discuss the implications of these findings for the
emergence of speech in humans.The authors acknowledge financial support from
the Spanish Ministry of Economy and Competitiveness
(MINECO), through the âSevero Ochoaâ
Programme for Centres/Units of Excellence in
R&D (SEV-2015-0490) to the BCBL, from European
Commission as Marie SkĆodowska-Curie fellowDLV-
792331 to L.P., fromMinisterio de Ciencia
E Innovacion by grant PSI2017-82563-P to A.G.S.,
and grant RTI2018-098317-B-I00 to M.O
A COGNITIVE ARCHITECTURE FOR AMBIENT INTELLIGENCE
LâAmbient Intelligence (AmI) Ăš caratterizzata dallâuso di sistemi pervasivi per
monitorare lâambiente e modificarlo secondo le esigenze degli utenti e rispettando
vincoli definiti globalmente. Questi sistemi non possono prescindere da requisiti
come la scalabilitĂ e la trasparenza per lâutente. Una tecnologia che consente di
raggiungere questi obiettivi Ăš rappresentata dalle reti di sensori wireless (WSN),
caratterizzate da bassi costi e bassa intrusivitĂ . Tuttavia, sebbene in grado di
effettuare elaborazioni a bordo dei singoli nodi, le WSN non hanno da sole le capacitĂ
di elaborazione necessarie a supportare un sistema intelligente; dâaltra parte
senza questa attivitĂ di pre-elaborazione la mole di dati sensoriali puĂČ facilmente
sopraffare un sistema centralizzato con unâeccessiva quantitĂ di dettagli superflui.
Questo lavoro presenta unâarchitettura cognitiva in grado di percepire e controllare
lâambiente di cui fa parte, basata su un nuovo approccio per lâestrazione
di conoscenza a partire dai dati grezzi, attraverso livelli crescenti di astrazione.
Le WSN sono utilizzate come strumento sensoriale pervasivo, le cui capacitĂ computazionali
vengono utilizzate per pre-elaborare i dati rilevati, in modo da consentire
ad un sistema centralizzato intelligente di effettuare ragionamenti di alto
livello.
Lâarchitettura proposta Ăš stata utilizzata per sviluppare un testbed dotato degli
strumenti hardware e software necessari allo sviluppo e alla gestione di applicazioni
di AmI basate su WSN, il cui obiettivo principale sia il risparmio energetico. Per
fare in modo che le applicazioni di AmI siano in grado di comunicare con il mondo
esterno in maniera affidabile, per richiedere servizi ad agenti esterni, lâarchitettura
Ăš stata arricchita con un protocollo di gestione distribuita della reputazione.
Ă stata inoltre sviluppata unâapplicazione di esempio che sfrutta le caratteristiche
del testbed, con lâobiettivo di controllare la temperatura in un ambiente
lavorativo. Questâapplicazione rileva la presenza dellâutente attraverso un modulo
per la fusione di dati multi-sensoriali basato su reti bayesiane, e sfrutta questa
informazione in un controllore fuzzy multi-obiettivo che controlla gli attuatori sulla
base delle preferenze dellâutente e del risparmio energetico.Ambient Intelligence (AmI) systems are characterized by the use of pervasive
equipments for monitoring and modifying the environment according to usersâ
needs, and to globally defined constraints. Furthermore, such systems cannot ignore
requirements about ubiquity, scalability, and transparency to the user. An
enabling technology capable of accomplishing these goals is represented by Wireless
Sensor Networks (WSNs), characterized by low-costs and unintrusiveness. However,
although provided of in-network processing capabilities, WSNs do not exhibit
processing features able to support comprehensive intelligent systems; on the other
hand, without this pre-processing activities the wealth of sensory data may easily
overwhelm a centralized AmI system, clogging it with superfluous details.
This work proposes a cognitive architecture able to perceive, decide upon, and
control the environment of which the system is part, based on a new approach to
knowledge extraction from raw data, that addresses this issue at different abstraction
levels. WSNs are used as the pervasive sensory tool, and their computational
capabilities are exploited to remotely perform preliminary data processing. A central
intelligent unit subsequently extracts higher-level concepts in order to carry on
symbolic reasoning. The aim of the reasoning is to plan a sequence of actions that
will lead the environment to a state as close as possible to the usersâ desires, taking
into account both implicit and explicit feedbacks from the users, while considering
global system-driven goals, such as energy saving. The proposed conceptual architecture
was exploited to develop a testbed providing the hardware and software
tools for the development and management of AmI applications based on WSNs,
whose main goal is energy saving for global sustainability. In order to make the
AmI system able to communicate with the external world in a reliable way, when
some services are required to external agents, the architecture was enriched with
a distributed reputation management protocol.
A sample application exploiting the testbed features was implemented for addressing
temperature control in a work environment. Knowledge about the userâs
presence is obtained through a multi-sensor data fusion module based on Bayesian
networks, and this information is exploited by a multi-objective fuzzy controller
that operates on actuators taking into account usersâ preference and energy consumption
constraints
Definition of Application Scenarios
The objective of D1 is to identify and analyse a set of application scenarios that, on the one hand, exemplify those application areas that might benefit from the technology being developed within the CORTEX project and, on the other hand, might serve as a source of requirements on this technology. Furthermore, at least a subset of the application scenarios considered here is expected to serve as source of demonstrator applications later in the projec
NASA space station automation: AI-based technology review
Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures
Interactive Execution Monitoring of Agent Teams
There is an increasing need for automated support for humans monitoring the
activity of distributed teams of cooperating agents, both human and machine. We
characterize the domain-independent challenges posed by this problem, and
describe how properties of domains influence the challenges and their
solutions. We will concentrate on dynamic, data-rich domains where humans are
ultimately responsible for team behavior. Thus, the automated aid should
interactively support effective and timely decision making by the human. We
present a domain-independent categorization of the types of alerts a plan-based
monitoring system might issue to a user, where each type generally requires
different monitoring techniques. We describe a monitoring framework for
integrating many domain-specific and task-specific monitoring techniques and
then using the concept of value of an alert to avoid operator overload. We use
this framework to describe an execution monitoring approach we have used to
implement Execution Assistants (EAs) in two different dynamic, data-rich,
real-world domains to assist a human in monitoring team behavior. One domain
(Army small unit operations) has hundreds of mobile, geographically distributed
agents, a combination of humans, robots, and vehicles. The other domain (teams
of unmanned ground and air vehicles) has a handful of cooperating robots. Both
domains involve unpredictable adversaries in the vicinity. Our approach
customizes monitoring behavior for each specific task, plan, and situation, as
well as for user preferences. Our EAs alert the human controller when reported
events threaten plan execution or physically threaten team members. Alerts were
generated in a timely manner without inundating the user with too many alerts
(less than 10 percent of alerts are unwanted, as judged by domain experts)
State-of-the-art on evolution and reactivity
This report starts by, in Chapter 1, outlining aspects of querying and updating resources on
the Web and on the Semantic Web, including the development of query and update languages
to be carried out within the Rewerse project.
From this outline, it becomes clear that several existing research areas and topics are of
interest for this work in Rewerse. In the remainder of this report we further present state of
the art surveys in a selection of such areas and topics. More precisely: in Chapter 2 we give
an overview of logics for reasoning about state change and updates; Chapter 3 is devoted to briefly describing existing update languages for the Web, and also for updating logic programs;
in Chapter 4 event-condition-action rules, both in the context of active database systems and
in the context of semistructured data, are surveyed; in Chapter 5 we give an overview of some relevant rule-based agents frameworks
On Agent Communication in Large Groups
The problem is fundamental and natural, yet deep - to simulate the simplest possible form of communication that can occur within a large multi-agent system. It would be prohibitive to try and survey all of the research on communication in general so we must restrict our focus. We will devote our efforts to synthetic communication occurring within large groups. In particular, we would like to discover a model for communication that will serve as an abstract model, a prototype, for simulating communication within large groups of biological organisms
The 1990 progress report and future plans
This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers
An enactive approach to perceptual augmentation in mobility
Event predictions are an important constituent of situation awareness, which is a key objective for many applications in human-machine interaction, in particular in driver assistance. This work focuses on facilitating event predictions in dynamic environments. Its primary contributions are 1) the theoretical development of an approach for enabling people to expand their sampling and understanding of spatiotemporal information, 2) the introduction of exemplary systems that are guided by this approach, 3) the empirical investigation of effects functional prototypes of these systems have on human behavior and safety in a range of simulated road traffic scenarios, and 4) a connection of the investigated approach to work on cooperative human-machine systems. More specific contents of this work are summarized as follows:
The first part introduces several challenges for the formation of situation awareness as a requirement for safe traffic participation. It reviews existing work on these challenges in the domain of driver assistance, resulting in an identification of the need to better inform drivers about dynamically changing aspects of a scene, including event probabilities, spatial and temporal distances, as well as a suggestion to expand the scope of assistance systems to start informing drivers about relevant scene elements at an early stage. Novel forms of assistance can be guided by different fundamental approaches that target either replacement, distribution, or augmentation of driver competencies. A subsequent differentiation of these approaches concludes that an augmentation-guided paradigm, characterized by an integration of machine capabilities into human feedback loops, can be advantageous for tasks that rely on active user engagement, the preservation of awareness and competence, and the minimization of complexity in human- machine interaction. Consequently, findings and theories about human sensorimotor processes are connected to develop an enactive approach that is consistent with an augmentation perspective on human-machine interaction. The approach is characterized by enabling drivers to exercise new sensorimotor processes through which safety-relevant spatiotemporal information may be sampled.
In the second part of this work, a concept and functional prototype for augmenting the perception of traffic dynamics is introduced as a first example for applying principles of this enactive approach. As a loose expression of functional biomimicry, the prototype utilizes a tactile inter- face that communicates temporal distances to potential hazards continuously through stimulus intensity. In a driving simulator study, participants quickly gained an intuitive understanding of the assistance without instructions and demonstrated higher driving safety in safety-critical highway scenarios. But this study also raised new questions such as whether benefits are due to a continuous time-intensity encoding and whether utility generalizes to intersection scenarios or highway driving with low criticality events. Effects of an expanded assistance prototype with lane-independent risk assessment and an option for binary signaling were thus investigated in a separate driving simulator study. Subjective responses confirmed quick signal understanding and a perception of spatial and temporal stimulus characteristics. Surprisingly, even for a binary assistance variant with a constant intensity level, participants reported perceiving a danger-dependent variation in stimulus intensity. They further felt supported by the system in the driving task, especially in difficult situations. But in contrast to the first study, this support was not expressed by changes in driving safety, suggesting that perceptual demands of the low criticality scenarios could be satisfied by existing driver capabilities. But what happens if such basic capabilities are impaired, e.g., due to poor visibility conditions or other situations that introduce perceptual uncertainty? In a third driving simulator study, the driver assistance was employed specifically in such ambiguous situations and produced substantial safety advantages over unassisted driving. Additionally, an assistance variant that adds an encoding of spatial uncertainty was investigated in these scenarios. Participants had no difficulties to understand and utilize this added signal dimension to improve safety. Despite being inherently less informative than spatially precise signals, users rated uncertainty-encoding signals as equally useful and satisfying. This appreciation for transparency of variable assistance reliability is a promising indicator for the feasibility of an adaptive trust calibration in human-machine interaction and marks one step towards a closer integration of driver and vehicle capabilities.
A complementary step on the driver side would be to increase transparency about the driverâs mental states and thus allow for mutual adaptation. The final part of this work discusses how such prerequisites of cooperation may be achieved by monitoring mental state correlates observable in human behavior, especially in eye movements. Furthermore, the outlook for an addition of cooperative features also raises new questions about the bounds of identity as well as practical consequences of human-machine systems in which co-adapting agents may exercise sensorimotor processes through one another.Die Vorhersage von Ereignissen ist ein Bestandteil des Situationsbewusstseins, dessen UnterstĂŒtzung ein wesentliches Ziel diverser Anwendungen im Bereich Mensch-Maschine Interaktion ist, insbesondere in der Fahrerassistenz. Diese Arbeit zeigt Möglichkeiten auf, Menschen bei Vorhersagen in dynamischen Situationen im StraĂenverkehr zu unterstĂŒtzen. Zentrale BeitrĂ€ge der Arbeit sind 1) eine theoretische Auseinandersetzung mit der Aufgabe, die menschliche Wahrnehmung und das VerstĂ€ndnis von raum-zeitlichen Informationen im StraĂenverkehr zu erweitern, 2) die EinfĂŒhrung beispielhafter Systeme, die aus dieser Betrachtung hervorgehen, 3) die empirische Untersuchung der Auswirkungen dieser Systeme auf das Nutzerverhalten und die Fahrsicherheit in simulierten Verkehrssituationen und 4) die VerknĂŒpfung der untersuchten AnsĂ€tze mit Arbeiten an kooperativen Mensch-Maschine Systemen. Die Arbeit ist in drei Teile gegliedert:
Der erste Teil stellt einige Herausforderungen bei der Bildung von Situationsbewusstsein vor, welches fĂŒr die sichere Teilnahme am StraĂenverkehr notwendig ist. Aus einem Vergleich dieses Ăberblicks mit frĂŒheren Arbeiten zeigt sich, dass eine Notwendigkeit besteht, Fahrer besser ĂŒber dynamische Aspekte von Fahrsituationen zu informieren. Dies umfasst unter anderem Ereigniswahrscheinlichkeiten, rĂ€umliche und zeitliche Distanzen, sowie eine frĂŒhere Signalisierung relevanter Elemente in der Umgebung.
Neue Formen der Assistenz können sich an verschiedenen grundlegenden AnsĂ€tzen der Mensch-Maschine Interaktion orientieren, die entweder auf einen Ersatz, eine Verteilung oder eine Erweiterung von Fahrerkompetenzen abzielen. Die Differenzierung dieser AnsĂ€tze legt den Schluss nahe, dass ein von Kompetenzerweiterung geleiteter Ansatz fĂŒr die BewĂ€ltigung jener Aufgaben von Vorteil ist, bei denen aktiver Nutzereinsatz, die Erhaltung bestehender Kompetenzen und Situationsbewusstsein gefordert sind. Im Anschluss werden Erkenntnisse und Theorien ĂŒber menschliche sensomotorische Prozesse verknĂŒpft, um einen enaktiven Ansatz der Mensch-Maschine Interaktion zu entwickeln, der einer erweiterungsgeleiteten Perspektive Rechnung trĂ€gt. Dieser Ansatz soll es Fahrern ermöglichen, sicherheitsrelevante raum-zeitliche Informationen ĂŒber neue sensomotorische Prozesse zu erfassen.
Im zweiten Teil der Arbeit wird ein Konzept und funktioneller Prototyp zur Erweiterung der Wahrnehmung von Verkehrsdynamik als ein erstes Beispiel zur Anwendung der Prinzipien dieses enaktiven Ansatzes vorgestellt. Dieser Prototyp nutzt vibrotaktile Aktuatoren zur Kommunikation von Richtungen und zeitlichen Distanzen zu möglichen Gefahrenquellen ĂŒber die Aktuatorposition und -intensitĂ€t. Teilnehmer einer Fahrsimulationsstudie waren in der Lage, in kurzer Zeit ein intuitives VerstĂ€ndnis dieser Assistenz zu entwickeln, ohne vorher ĂŒber die FunktionalitĂ€t unterrichtet worden zu sein. Sie zeigten zudem ein erhöhtes MaĂ an Fahrsicherheit in kritischen Verkehrssituationen. Doch diese Studie wirft auch neue Fragen auf, beispielsweise, ob der Sicherheitsgewinn auf kontinuierliche Distanzkodierung zurĂŒckzufĂŒhren ist und ob ein Nutzen auch in weiteren Szenarien vorliegen wĂŒrde, etwa bei Kreuzungen und weniger kritischem longitudinalen Verkehr. Um diesen Fragen nachzugehen, wurden Effekte eines erweiterten Prototypen mit spurunabhĂ€ngiger KollisionsprĂ€diktion, sowie einer Option zur binĂ€ren Kommunikation möglicher Kollisionsrichtungen in einer weiteren Fahrsimulatorstudie untersucht. Auch in dieser Studie bestĂ€tigen die subjektiven Bewertungen ein schnelles VerstĂ€ndnis der Signale und eine Wahrnehmung rĂ€umlicher und zeitlicher Signalkomponenten. Ăberraschenderweise berichteten Teilnehmer gröĂtenteils auch nach der Nutzung einer binĂ€ren Assistenzvariante, dass sie eine gefahrabhĂ€ngige Variation in der IntensitĂ€t von taktilen Stimuli wahrgenommen hĂ€tten. Die Teilnehmer fĂŒhlten sich mit beiden Varianten in der Fahraufgabe unterstĂŒtzt, besonders in Situationen, die von ihnen als kritisch eingeschĂ€tzt wurden. Im Gegensatz zur ersten Studie hat sich diese gefĂŒhlte UnterstĂŒtzung nur geringfĂŒgig in einer messbaren SicherheitsverĂ€nderung widergespiegelt. Dieses Ergebnis deutet darauf hin, dass die Wahrnehmungsanforderungen der Szenarien mit geringer KritikalitĂ€t mit den vorhandenen FahrerkapazitĂ€ten erfĂŒllt werden konnten.
Doch was passiert, wenn diese FĂ€higkeiten eingeschrĂ€nkt werden, beispielsweise durch schlechte Sichtbedingungen oder Situationen mit erhöhter AmbiguitĂ€t? In einer dritten Fahrsimulatorstudie wurde das Assistenzsystem in speziell solchen Situationen eingesetzt, was zu substantiellen Sicherheitsvorteilen gegenĂŒber unassistiertem Fahren gefĂŒhrt hat. ZusĂ€tzlich zu der vorher eingefĂŒhrten Form wurde eine neue Variante des Prototyps untersucht, welche rĂ€umliche Unsicherheiten der Fahrzeugwahrnehmung in taktilen Signalen kodiert. Studienteilnehmer hatten keine Schwierigkeiten, diese zusĂ€tzliche Signaldimension zu verstehen und die Information zur Verbesserung der Fahrsicherheit zu nutzen. Obwohl sie inherent weniger informativ sind als rĂ€umlich prĂ€zise Signale, bewerteten die Teilnehmer die Signale, die die Unsicherheit ĂŒbermitteln, als ebenso nĂŒtzlich und zufriedenstellend. Solch eine WertschĂ€tzung fĂŒr die Transparenz variabler InformationsreliabilitĂ€t ist ein vielversprechendes Indiz fĂŒr die Möglichkeit einer adaptiven Vertrauenskalibrierung in der Mensch-Maschine Interaktion. Dies ist ein Schritt hin zur einer engeren Integration der FĂ€higkeiten von Fahrer und Fahrzeug.
Ein komplementÀrer Schritt wÀre eine Erweiterung der Transparenz mentaler ZustÀnde des Fahrers, wodurch eine wechselseitige Anpassung von Mensch und Maschine möglich wÀre.
Der letzte Teil dieser Arbeit diskutiert, wie diese Transparenz und weitere Voraussetzungen von Mensch-Maschine Kooperation erfĂŒllt werden könnten, indem etwa Korrelate mentaler ZustĂ€nde, insbesondere ĂŒber das Blickverhalten, ĂŒberwacht werden. Des Weiteren ergeben sich mit Blick auf zusĂ€tzliche kooperative FĂ€higkeiten neue Fragen ĂŒber die Definition von IdentitĂ€t, sowie ĂŒber die praktischen Konsequenzen von Mensch-Maschine Systemen, in denen ko-adaptive Agenten sensomotorische Prozesse vermittels einander ausĂŒben können
- âŠ