Thursday, May 31, 2007

Anticipation and Design

In the latest Design Studies:

Towards an Anticipatory View of Design
Theodore Zamenopoulos and Katerina Alexiou


Anticipation indicates the capacity to act in preparation for a certain effect or future state of the world. Although the link between anticipation and previous termdesignnext term has not received particular attention in previous termdesignnext term research, it is a fundamental one. In the paper we review the concept of anticipation and discuss its meaning for previous termdesignnext term research. We further argue that in order to develop an previous termanticipatory view of designnext term it is necessary to move beyond long-established paradigms and abstractions such as those of machine, evolution and control. Based on a conceptual and methodological framework proposed by Robert Rosen we elaborate such an previous termanticipatory viewnext term that establishes the uniqueness of previous termdesignnext term compared to these paradigms.

Tuesday, May 29, 2007

Multi-agent Learning

The latest issue of Artificial Intelligence is devoted to perspectives on multi-agent learning.

Artificial Intelligence, Volume 171, Issue 7, Pages 363-452 (May 2007)Foundations of Multi-Agent Learning Edited by Rakesh Vohra and Michael Wellman

Limits on memory and reasoning

From Trends in Cognitive Sciences:

Separating cognitive capacity from knowledge: a new hypothesis
Graeme S. Halford, , Nelson Cowan and Glenda Andrews

Abstract: We propose that working memory and reasoning share related capacity limits. These limits are quantified in terms of the number of items that can be kept active in working memory, and the number of interrelationships between elements that can be kept active in reasoning. The latter defines the complexity of reasoning problems and the processing loads they impose. Principled procedures for measuring, controlling or limiting recoding and other strategies for reducing memory and processing loads have opened up new research opportunities, and yielded orderly quantification of capacity limits in both memory and reasoning. We argue that both types of limit might be based on the limited ability to form and preserve bindings between elements in memory.

Spatial navigation

From the latest issue of the journal Cognition, I found this interesting paper on how virtual taxi-cab drivers, use road signs and other cues to navigate.

Learning your way around town: How virtual taxicab drivers learn to use both layout and landmark information,
Ehren L. Newmana, Jeremy B. Caplana, Matthew P. Kirschena, Igor O. Koroleva, Robert Sekulera and Michael J. Kahana

Abstract: By having subjects drive a virtual taxicab through a computer-rendered town, we examined how landmark and layout information interact during spatial navigation. Subject-drivers searched for passengers, and then attempted to take the most efficient route to the requested destinations (one of several target stores). Experiment 1 demonstrated that subjects rapidly learn to find direct paths from random pickup locations to target stores. Experiment 2 varied the degree to which landmark and layout cues were preserved across two successively learned towns. When spatial layout was preserved, transfer was low if only target stores were altered, and high if both target stores and surrounding buildings were altered, even though in the latter case all local views were changed. This suggests that subjects can rapidly acquire a survey representation based on the spatial layout of the town and independent of local views, but that subjects will rely on local views when present, and are harmed when associations between previously learned landmarks are disrupted. We propose that spatial navigation reflects a hierarchical system in which either layout or landmark information is sufficient for orienting and wayfinding; however, when these types of cues conflict, landmarks are preferentially used.

On ontologies

The latest issue of the International Journal of Human-Computer Studies is a special issue on the role of ontologies in knowledge representation.

For a list of all publications in the issue, see here.

These are the two papers I found interesting:

Knowledge representation with ontologies: Present challenges—Future possibilities
Christopher Brewster and Kieron O’Hara

Abstract: Ontologies have become the knowledge representation medium of choice in recent years for a range of computer science specialities including the Semantic Web, Agents, and Bio-informatics. There has been a great deal of research and development in this area combined with hype and reaction. This special issue is concerned with the limitations of ontologies and how these can be addressed, together with a consideration of how we can circumvent or go beyond these constraints. The introduction places the discussion in context and presents the papers included in this issue.

Knowledge representation with ontologies: Present challenges—Future possibilities
Christopher Brewster and Kieron O’Hara

Abstract: In information systems that support knowledge-discovery applications such as scientific exploration, reliance on highly structured ontologies as data-organization aids can be limiting. With current computational aids to science work, the human knowledge that creates meaning out of analyses is often only recorded when work reaches publication—or worse, left unrecorded altogether—for lack of an ontological model for scientific concepts that can capture knowledge as it is created and used. We argue for an approach to representing scientific concepts that reflects (1) the situated processes of science work, (2) the social construction of knowledge, and (3) the emergence and evolution of understanding over time. In this model, knowledge is the result of collaboration, negotiation, and manipulation by teams of researchers. Capturing the situations in which knowledge is created and used helps these collaborators discover areas of agreement and discord, while allowing individual inquirers to maintain different perspectives on the same information. The capture of provenance information allows historical trails of reasoning to be reconstructed, allowing end users to evaluate the utility and trustworthiness of knowledge representations. We present a proof-of-concept system, called Codex, based on this situated knowledge model. Codex supports visualization of knowledge structures through concept mapping, and enables inference across those structures. The proof-of-concept is deployed in the domain of geoscience to support distributed teams of learners and researchers.