Linking neural and symbolic representation and processing of conceptual structures

We compare and discuss representations in two cognitive architectures aimed at representing and processing complex conceptual (sentence-like) structures. First is the Neural Blackboard Architecture (NBA), which aims to account for representation and processing of complex and combinatorial conceptual structures in the brain. Second is IDyOT (Information Dynamics of Thinking), which derives sentence-like structures by learning statistical sequential regularities over a suitable corpus. Although IDyOT is designed at a level more abstract than the neural, so it is a model of cognitive function, rather than neural processing, there are strong similarities between the composite structures developed in IDyOT and the NBA. We hypothesize that these similarities form the basis of a combined architecture in which the individual strengths of each architecture are integrated. We outline and discuss the characteristics of this combined architecture, emphasizing the representation and processing of conceptual structures

Developing Self-Awareness in Robots via Inner Speech

The experience of inner speech is a common one. Such a dialogue accompanies the introspection of mental life and fulfills essential roles in human behavior, such as self-restructuring, self-regulation, and re-focusing on attentional resources. Although the underpinning of inner speech is mostly investigated in psychological and philosophical fields, the research in robotics generally does not address such a form of self-aware behavior. Existing models of inner speech inspire computational tools to provide a robot with this form of self-awareness. Here, the widespread psychological models of inner speech are reviewed, and a cognitive architecture for a robot implementing such a capability is outlined in a simplified setup

AFRANCI : multi-layer architecture for cognitive agents

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201

Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence

General Attention Mechanism for Artificial Intelligence Systems

In the domain of intelligent systems, the management of mental resources is typically called “attention”. Attention exists because all moderately complex environments – and the real-world environments of everyday life in particular – are a source of vastly greater information than can be processed in real-time by available cognitive resources of any known intelligence, human or otherwise. General-purpose artificial intelligence (AI) systems operating with limited resources under time-constraints in such environments must select carefully which information will be processed and which will be ignored. Even in the (rare) cases where sufficient resources may be available, attention could help make better use of them. All real-world tasks come with time limits, and managing these is a key part of the role of intelligence. Many AI researchers ignore this fact. As a result, the majority of existing AI architectures is incorrectly based on an (explicit or implicit) assumption of infinite or sufficient computational resources. Attention has not yet been recognized as a key cognitive process of AI systems and in particular not of artificial general intelligence systems. This dissertation argues for the absolute necessity of an attention mechanism for artificial general intelligence (AGI) architectures. We examine several issues related to attention and resource management, review prior work on these topics in cognitive psychology and AI, and present a design for a general attention mechanism for AGI systems. The proposed design – inspired by constructivist AI methodologies – aims at architectural and modal independence, and comprehensively addresses and integrates all principal factors associated with attention to date.Stjórnun og ráðstöfun hugarafls í greindum kerfum er oftast kölluð "athygli". Athygli er til staðar þar sem öll flókin umhverfi – sérstaklega raunheimurinn – eru uppspretta margfalt meiri upplýsingamagns en nokkur vitsmunavera getur unnið úr í rauntíma. Kerfi með alhliða gervigreind, sem starfa með takmarkaða reiknigetu undir margvíslegum tímaskorðum, verða að velja vandlega hvaða upplýsingum þau vinna úr og hvaða upplýsingar þau leiða hjá sér. Jafnvel í þeim (sjaldgæfu) tilfellum þar sem næganleg reiknigeta gæti verið til staðar gæti athygli bætt nýtingu hennar. Öll verkefni í raunheiminum hafa tímaskorður og meðhöndlun þeirra skorða er eitt lykilhlutverk greindar. Fjöldi rannsakenda á sviði gervigreindar hafa þó hunsað þessa staðreynd og þar af leiðandi er meirihluti þeirra gervigreindarkerfa sem hafa verið smíðuð ranglega byggður á þeirri forsendu að kerfin búi yfir óendanlegri reiknigetu. Athygli hefur hingað til ekki fengið verðskuldaða áherslu sem lykilatriði í hönnun og hugarferli gervigreindarkerfa. Í þessari ritgerð er sýnt fram á að athygli er algjörlega nauðsynleg alhliða gervigreindarkerfum. Margvísleg málefni tengd athygli og stjórnun aðfanga (reiknigetu, minnis og tíma) eru rannsökuð, farið er yfir fyrri rannsóknir í hugfræði og gervigreind og hönnun alhliða athyglisstýringar fyrir gervigreindarkerfi er kynnt til sögunnar. Aðferðafræði sjálfsþróunar við gerð gervigreindarkerfa er fylgt í hönnuninni, og reynt er að fylgja leiðum sem eru óháðar arkitektúr og skynrása kerfisins, og jafnframt nálgast á heildrænan hátt alla helstu þætti sem hafa hingað til verið tengdir athygli.This work was supported in part by the EU-funded project HUMANOBS: Humanoids That Learn Socio-Communicative Skills Through Observation, contract no. FP7-STREP-231453 (www.humanobs.org), and by grants from Rannís, Iceland

Foundations of Human-Aware Planning -- A Tale of Three Models

abstract: A critical challenge in the design of AI systems that operate with humans in the loop is to be able to model the intentions and capabilities of the humans, as well as their beliefs and expectations of the AI system itself. This allows the AI system to be "human- aware" -- i.e. the human task model enables it to envisage desired roles of the human in joint action, while the human mental model allows it to anticipate how its own actions are perceived from the point of view of the human. In my research, I explore how these concepts of human-awareness manifest themselves in the scope of planning or sequential decision making with humans in the loop. To this end, I will show (1) how the AI agent can leverage the human task model to generate symbiotic behavior; and (2) how the introduction of the human mental model in the deliberative process of the AI agent allows it to generate explanations for a plan or resort to explicable plans when explanations are not desired. The latter is in addition to traditional notions of human-aware planning which typically use the human task model alone and thus enables a new suite of capabilities of a human-aware AI agent. Finally, I will explore how the AI agent can leverage emerging mixed-reality interfaces to realize effective channels of communication with the human in the loop.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Do You Know What I Know?:Situational Awareness of Co-located Teams in Multidisplay Environments

Modern collaborative environments often provide an overwhelming amount of visual information on multiple displays. In complex project settings, the amount of visual information on multiple displays, and the multitude of personal and shared interaction devices in these environments can reduce the awareness of team members on ongoing activities, the understanding of shared visualisations, and the awareness of who is in control of shared artifacts. Research reported in this thesis addresses the situational awareness (SA) support of co-located teams working on team projects in multidisplay environments. Situational awareness becomes even more critical when the content of multiple displays changes rapidly, and when these provide large amounts of information. This work aims at getting insights into design and evaluation of shared display visualisations that afford situational awareness and group decision making. This thesis reports the results of three empirical user studies in three different domains: life science experimentation, decision making in brainstorming teams, and agile software development. The first and the second user studies evaluate the impact of the Highlighting-on-Demand and the Chain-of-Thoughts SA on the group decision-making and awareness. The third user study presents the design and evaluation of a shared awareness display for software teams. Providing supportive visualisations on a shared large display, we aimed at reducing the distraction from the primary task, enhancing the group decision-making process and the perceived task performance

Advances in Multiple Viewpoint Systems and Applications in Modelling Higher Order Musical Structure

PhDStatistical approaches are capable of underpinning strong models of musical structure, perception, and cognition. Multiple viewpoint systems are probabilistic models of sequential prediction that aim to capture the multidimensional aspects of a symbolic domain with predictions from multiple finite-context models combined in an information theoretically informed way. Information theory provides an important grounding for such models. In computational terms, information content is an empirical measure of compressibility for model evaluation, and entropy a powerful weighting system for combining predictions from multiple models. In perceptual terms, clear parallels can be drawn between information content and surprise, and entropy and certainty. In cognitive terms information theory underpins explanatory models of both musical representation and expectation. The thesis makes two broad contributions to the field of statistical modelling of music cognition: firstly, advancing the general understanding of multiple viewpoint systems, and, secondly, developing bottom-up, statistical learning methods capable of capturing higher order structure. In the first category, novel methods for predicting multiple basic attributes are empirically tested, significantly outperforming established methods, and refuting the assumption found in the literature that basic attributes are statistically independent from one another. Additionally, novel techniques for improving the prediction of derived viewpoints (viewpoints that abstract information away from whatever musical surface is under consideration) are introduced and analysed, and their relation with cognitive representations explored. Finally, the performance and suitability of an established algorithm that automatically constructs locally optimal multiple viewpoint systems is tested. In the second category, the current research brings together a number of existing statistical methods for segmentation and modelling musical surfaces with the aim of representing higher-order structure. A comprehensive review and empirical evaluation of these information theoretic segmentation methods is presented. Methods for labelling higher order segments, akin to layers of abstraction in a representation, are empirically evaluated and the cognitive implications explored. The architecture and performance of the models are assessed from cognitive and musicological perspectives.Media and Arts Technology programme, EPSRC Doctoral Training Centre EP/G03723X/1

Conference on Intelligent Robotics in Field, Factory, Service, and Space (CIRFFSS 1994), volume 1

The AIAA/NASA Conference on Intelligent Robotics in Field, Factory, Service, and Space (CIRFFSS '94) was originally proposed because of the strong belief that America's problems of global economic competitiveness and job creation and preservation can partly be solved by the use of intelligent robotics, which are also required for human space exploration missions. Individual sessions addressed nuclear industry, agile manufacturing, security/building monitoring, on-orbit applications, vision and sensing technologies, situated control and low-level control, robotic systems architecture, environmental restoration and waste management, robotic remanufacturing, and healthcare applications

Mind and Matter

Do brains create material reality in thinking processes or is it the other way around, with things shaping the mind? Where is the location of meaning-making? How do neural networks become established by means of multimodal pattern replications, and how are they involved in conceptualization? How are resonance textures within cellular entities extended in the body and the mind by means of mirroring processes? In which ways do they correlate to consciousness and self-consciousness? Is it possible to explain out-of-awareness unconscious processes? What holds together the relationship between experiential reality, bodily processes like memory, reason, or imagination, and sign-systems and simulation structures like metaphor and metonymy visible in human language? This volume attempts to answer some of these questions