159 research outputs found
Control of Mobile Robots Using the Soar Cognitive Architecture
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77099/1/AIAA-37056-144.pd
Consciosusness in Cognitive Architectures. A Principled Analysis of RCS, Soar and ACT-R
This report analyses the aplicability of the principles of consciousness developed in the ASys project to three of the most relevant cognitive architectures. This is done in relation to their aplicability to build integrated control systems and studying their support for general mechanisms of real-time consciousness.\ud
To analyse these architectures the ASys Framework is employed. This is a conceptual framework based on an extension for cognitive autonomous systems of the General Systems Theory (GST).\ud
A general qualitative evaluation criteria for cognitive architectures is established based upon: a) requirements for a cognitive architecture, b) the theoretical framework based on the GST and c) core design principles for integrated cognitive conscious control systems
Using fuzzy logic to integrate neural networks and knowledge-based systems
Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems
Approaches to coupling connectionist and expert systems in intelligent manufacturing
Artificial neural networks are successfully applied in different fields of manufacturing, mostly where multisensor integration, robustness, real-timeness, and learning abilities are needed. Since the higher levels of the control and the monitoring hierarchy require symbolic knowledge representation and processing techniques, the integrated use of the symbolic and subsymbolic approaches is straightforward The paper describes two hybrid artificial intelligence systems for control and monitoring of manufacturing processes on different hardware and software bases, The first experiences gained by their usage are outlined. Finally, further possible applications of these hybrid solutions in an intelligent manufacturing environment are enumerated. (C) 1997 Elsevier Science B.V
Artificial intelligence approaches for the generation and assessment of believable human-like behaviour in virtual characters
Having artificial agents to autonomously produce human-like behaviour is one of the most ambitious original goals of Artificial Intelligence (AI) and remains an open problem nowadays. The imitation game originally proposed by Turing constitute a very effective method to prove the indistinguishability of an artificial agent. The behaviour of an agent is said to be indistinguishable from that of a human when observers (the so-called judges in the Turing test) can not tell apart humans and non-human agents. Different environments, testing protocols, scopes and problem domains can be established to develop limited versions or variants of the original Turing test. In this paper we use a specific version of the Turing test, based on the international BotPrize competition, built in a First-Person Shooter video game, where both human players and non-player characters interact in complex virtual environments. Based on our past experience both in the BotPrize competition and other robotics and computer game AI applications we have developed three new more advanced controllers for believable agents: two based on a combination of the CERA-CRANIUM and SOAR cognitive architectures and other based on ADANN, a system for the automatic evolution and adaptation of artificial neural networks. These two new agents have been put to the test jointly with CCBot3, the winner of BotPrize 2010 competition [1], and have showed a significant improvement in the humanness ratio. Additionally, we have confronted all these bots to both First-person believability assessment (BotPrize original judging protocol) and Third-person believability assess- ment, demonstrating that the active involvement of the judge has a great impact in the recognition of human-like behaviour.MICINN -Ministerio de Ciencia e InnovaciĂłn(FCT-13-7848
Design an evaluation of RoboCup humanoid goalie
P. 19-26In this article we describe the ethological inspired
architecture we have developed and how it has been used to
implement a humanoid goalkeeper according to the regulations of
the two-legged Standard Platform League of the RoboCup Federation. We present relevant concepts borrowed from ethology that
we have successfully used for generating autonomous behaviours
in mobile robotics, such as the use of ethograms in robotic pets
or the ideas of schemata, or the use of ďŹxed actions patterns
to implement reactivity. Then we discuss the implementation of
this architecture on the Nao biped robot. Finally, we propose a
method for its evaluation and validation and analyse the results
obtained during RoboCup real competition, which allowed us to
test ďŹrst hand how it worked in a real environmentS
A COGNITIVE ARCHITECTURE FOR AMBIENT INTELLIGENCE
LâAmbient Intelligence (AmI) è 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 è 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 attività di pre-elaborazione la mole di dati sensoriali può 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 è 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
è 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
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