125 research outputs found
Towards Integrated Cognitive and Interface Analysis
AbstractUsing cognitive architectures to analyse the usability of human-computer interfaces is an extensively investigated strategy. A particularly powerful way to perform such analysis is through syndetic modelling, where both the interface and the chosen cognitive model are described in the same specification framework; allowing the combined behaviour of the two to be analysed. This paper proposes LOTOS as a syndetic modelling language. We highlight four reasons why syndetic modelling is so difficult and show how the LOTOS notation addresses each of these four reasons
Computational Aspects of Asynchronous CA
This work studies some aspects of the computational power of fully
asynchronous cellular automata (ACA). We deal with some notions of simulation
between ACA and Turing Machines. In particular, we characterize the updating
sequences specifying which are "universal", i.e., allowing a (specific family
of) ACA to simulate any TM on any input. We also consider the computational
cost of such simulations
Systems, interactions and macrotheory
A significant proportion of early HCI research was guided by one very clear vision: that the existing theory base in psychology and cognitive science could be developed to yield engineering tools for use in the interdisciplinary context of HCI design. While interface technologies and heuristic methods for behavioral evaluation have rapidly advanced in both capability and breadth of application, progress toward deeper theory has been modest, and some now believe it to be unnecessary. A case is presented for developing new forms of theory, based around generic “systems of interactors.” An overlapping, layered structure of macro- and microtheories could then serve an explanatory role, and could also bind together contributions from the different disciplines. Novel routes to formalizing and applying such theories provide a host of interesting and tractable problems for future basic research in HCI
Continuity in cognition
Designing for continuous interaction requires
designers to consider the way in which human users can
perceive and evaluate an artefact’s observable behaviour,
in order to make inferences about its state and plan, and
execute their own continuous behaviour. Understanding
the human point of view in continuous interaction requires
an understanding of human causal reasoning, of
the way in which humans perceive and structure the
world, and of human cognition. We present a framework
for representing human cognition, and show briefly how it
relates to the analysis of structure in continuous interaction,
and the ways in which it may be applied in design
Reasoning about order errors in interaction
Reliability of an interactive system depends on users as well as the device implementation. User errors can result in catastrophic system
failure. However, work from the field of cognitive science shows that
systems can be designed so as to completely eliminate whole classes of
user errors. This means that user errors should also fall within the remit
of verification methods. In this paper we demonstrate how the HOL
theorem prover [7] can be used to detect and prove the absence of the
family of errors known as order errors. This is done by taking account
of the goals and knowledge of users. We provide an explicit generic user
model which embodies theory from the cognitive sciences about the way
people are known to act. The user model describes action based on user
communication goals. These are goals that a user adopts based on their
knowledge of the task they must perform to achieve their goals. We use
a simple example of a vending machine to demonstrate the approach.
We prove that a user does achieve their goal for a particular design of
machine. In doing so we demonstrate that communication goal based
errors cannot occur
Relational Approach to Knowledge Engineering for POMDP-based Assistance Systems as a Translation of a Psychological Model
Assistive systems for persons with cognitive disabilities (e.g. dementia) are
difficult to build due to the wide range of different approaches people can
take to accomplishing the same task, and the significant uncertainties that
arise from both the unpredictability of client's behaviours and from noise in
sensor readings. Partially observable Markov decision process (POMDP) models
have been used successfully as the reasoning engine behind such assistive
systems for small multi-step tasks such as hand washing. POMDP models are a
powerful, yet flexible framework for modelling assistance that can deal with
uncertainty and utility. Unfortunately, POMDPs usually require a very labour
intensive, manual procedure for their definition and construction. Our previous
work has described a knowledge driven method for automatically generating POMDP
activity recognition and context sensitive prompting systems for complex tasks.
We call the resulting POMDP a SNAP (SyNdetic Assistance Process). The
spreadsheet-like result of the analysis does not correspond to the POMDP model
directly and the translation to a formal POMDP representation is required. To
date, this translation had to be performed manually by a trained POMDP expert.
In this paper, we formalise and automate this translation process using a
probabilistic relational model (PRM) encoded in a relational database. We
demonstrate the method by eliciting three assistance tasks from non-experts. We
validate the resulting POMDP models using case-based simulations to show that
they are reasonable for the domains. We also show a complete case study of a
designer specifying one database, including an evaluation in a real-life
experiment with a human actor
Technologic Innovation Process Improvement
AbstractBefore decade 80 the presented models for innovation process were assumed that were based on simple linear process. Then after more researches nonlinear processes were evaluated and variety of researchers tried to recognize innovation process. In this paper the process of technologic innovation and different models in this area are compared
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