8,610 research outputs found
Self-directedness, integration and higher cognition
In this paper I discuss connections between self-directedness, integration and higher cognition. I present a model of self-directedness as a basis for approaching higher cognition from a situated cognition perspective. According to this model increases in sensorimotor complexity create pressure for integrative higher order control and learning processes for acquiring information about the context in which action occurs. This generates complex articulated abstractive information processing, which forms the major basis for higher cognition. I present evidence that indicates that the same integrative characteristics found in lower cognitive process such as motor adaptation are present in a range of higher cognitive process, including conceptual learning. This account helps explain situated cognition phenomena in humans because the integrative processes by which the brain adapts to control interaction are relatively agnostic concerning the source of the structure participating in the process. Thus, from the perspective of the motor control system using a tool is not fundamentally different to simply controlling an arm
Towards musical interaction : 'Schismatics' for e-violin and computer.
This paper discusses the evolution of the Max/MSP
patch used in schismatics (2007, rev. 2010) for electric
violin (Violectra) and computer, by composer Sam
Hayden in collaboration with violinist Mieko Kanno.
schismatics involves a standard performance paradigm
of a fixed notated part for the e-violin with sonically unfixed
live computer processing. Hayden was unsatisfied
with the early version of the piece: the use of attack
detection on the live e-violin playing to trigger stochastic
processes led to an essentially reactive behaviour in the
computer, resulting in a somewhat predictable one-toone
sonic relationship between them. It demonstrated
little internal relationship between the two beyond an
initial e-violin ‘action’ causing a computer ‘event’. The
revisions in 2010, enabled by an AHRC Practice-Led
research award, aimed to achieve 1) a more interactive
performance situation and 2) a subtler and more
‘musical’ relationship between live and processed
sounds. This was realised through the introduction of
sound analysis objects, in particular machine listening
and learning techniques developed by Nick Collins. One
aspect of the programming was the mapping of analysis
data to synthesis parameters, enabling the computer
transformations of the e-violin to be directly related to
Kanno’s interpretation of the piece in performance
How active perception and attractor dynamics shape perceptual categorization: A computational model
We propose a computational model of perceptual categorization that fuses elements of grounded and sensorimotor theories of cognition with dynamic models of decision-making. We assume that category information consists in anticipated patterns of agent–environment interactions that can be elicited through overt or covert (simulated) eye movements, object manipulation, etc. This information is firstly encoded when category information is acquired, and then re-enacted during perceptual categorization. The perceptual categorization consists in a dynamic competition between attractors that encode the sensorimotor patterns typical of each category; action prediction success counts as ‘‘evidence’’ for a given category and contributes to falling into the corresponding attractor. The evidence accumulation process is guided by an active perception loop, and the active exploration of objects (e.g., visual exploration) aims at eliciting expected sensorimotor patterns that count as evidence for the object category. We present a computational model incorporating these elements and describing action prediction, active perception, and attractor dynamics as key elements of perceptual categorizations. We test the model in three simulated perceptual categorization tasks, and we discuss its relevance for grounded and sensorimotor theories of cognition.Peer reviewe
The evolution of representation in simple cognitive networks
Representations are internal models of the environment that can provide
guidance to a behaving agent, even in the absence of sensory information. It is
not clear how representations are developed and whether or not they are
necessary or even essential for intelligent behavior. We argue here that the
ability to represent relevant features of the environment is the expected
consequence of an adaptive process, give a formal definition of representation
based on information theory, and quantify it with a measure R. To measure how R
changes over time, we evolve two types of networks---an artificial neural
network and a network of hidden Markov gates---to solve a categorization task
using a genetic algorithm. We find that the capacity to represent increases
during evolutionary adaptation, and that agents form representations of their
environment during their lifetime. This ability allows the agents to act on
sensorial inputs in the context of their acquired representations and enables
complex and context-dependent behavior. We examine which concepts (features of
the environment) our networks are representing, how the representations are
logically encoded in the networks, and how they form as an agent behaves to
solve a task. We conclude that R should be able to quantify the representations
within any cognitive system, and should be predictive of an agent's long-term
adaptive success.Comment: 36 pages, 10 figures, one Tabl
From Verbs to Tasks: An Integrated Account of Learning Tasks from Situated Interactive Instruction.
Intelligent collaborative agents are becoming common in the human society. From virtual assistants such as Siri and Google Now to assistive robots, they contribute to human activities in a variety of ways. As they become more pervasive, the challenge of customizing them to a variety of environments and tasks becomes critical. It is infeasible for engineers to program them for each individual use. Our research aims at building interactive robots and agents that adapt to new environments autonomously by interacting with human users using natural modalities.
This dissertation studies the problem of learning novel tasks from human-agent dialog. We propose a novel approach for interactive task learning, situated interactive instruction (SII), and investigate approaches to three computational challenges that arise in designing SII agents: situated comprehension, mixed-initiative interaction, and interactive task learning. We propose a novel mixed-modality grounded representation for task verbs which encompasses their lexical, semantic, and
task-oriented aspects. This representation is useful in situated comprehension and can be learned through human-agent interactions. We introduce the Indexical Model of comprehension that can exploit
extra-linguistic contexts for resolving semantic ambiguities in situated comprehension of task commands. The Indexical model is integrated with a mixed-initiative interaction model that facilitates
a flexible task-oriented human-agent dialog. This dialog serves as the basis of interactive task learning. We propose an interactive variation of explanation-based learning that can acquire the proposed
representation. We demonstrate that our learning paradigm is efficient, can transfer knowledge between structurally similar tasks, integrates agent-driven exploration with instructional learning, and can acquire several tasks. The methods proposed in this thesis are integrated in Rosie - a generally instructable agent developed in the Soar cognitive architecture and embodied on a table-top robot.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111573/1/shiwali_1.pd
Learning epistemic actions in model-free memory-free reinforcement learning: experiments with a neuro-robotic model
Passive sensory processing is often insufficient to guide biological organisms in complex environments. Rather, behaviourally relevant information can be accessed by performing so-called epistemic actions that explicitly aim at unveiling hidden information. However, it is still unclear how an autonomous agent can learn epistemic actions and how it can use them adaptively. In this work, we propose a definition of epistemic actions for POMDPs that derive from their characterizations in cognitive science and classical planning literature. We give theoretical insights about how partial observability and epistemic actions can affct the learning process and performance in the extreme conditions of model-free and memory-free reinforcement learning where hidden information cannot be represented. We finally investigate these concepts using an integrated eye-arm neural architecture for robot control, which can use its effctors to execute epistemic actions and can exploit the actively gathered information to effiently accomplish a seek-and-reach task
Probabilistic Hybrid Action Models for Predicting Concurrent Percept-driven Robot Behavior
This article develops Probabilistic Hybrid Action Models (PHAMs), a realistic
causal model for predicting the behavior generated by modern percept-driven
robot plans. PHAMs represent aspects of robot behavior that cannot be
represented by most action models used in AI planning: the temporal structure
of continuous control processes, their non-deterministic effects, several modes
of their interferences, and the achievement of triggering conditions in
closed-loop robot plans.
The main contributions of this article are: (1) PHAMs, a model of concurrent
percept-driven behavior, its formalization, and proofs that the model generates
probably, qualitatively accurate predictions; and (2) a resource-efficient
inference method for PHAMs based on sampling projections from probabilistic
action models and state descriptions. We show how PHAMs can be applied to
planning the course of action of an autonomous robot office courier based on
analytical and experimental results
- …