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Research group overview

Common Sense

Principal investigator: Kenneth Haase

Despite decades of sustained exponential improvement in performance and capacity, computer applications and services remain brittle, difficult to use, awkward to learn, and unreliable outside of narrow contexts. Much of the reason for this failure is that they lack the common sense --- the ability to adapt and understand --- of a six-year old child. The Common Sense group at Media Lab Europe is giving computer applications the adaptability, breadth, and integration to interact fluently, fail gently, and take initiative reliably. Systems with common sense learn from experience, draw on deep backgrounds of understanding, and understand the world in multiple divergent but mutually supporting ways.

Projects

Common Sense T9
Nick Hawes, John Kelleher
Predictive text input using the T9 approach is currently the de-facto standard for text entry in the mobile domain. One problem it faces is the selection of which word to present to the user when more than one word matches an input sequence. The standard T9 approach selects words using word frequencies, ignoring the context provided by previous interactions with the system. The goal of the Common Sense T9 project is to investigate ways in which common sense knowledge can be used to augment the standard T9 approach to text prediction, improving the number of words it correctly predicts first time.

Common Sensing
Ken Haase
Common Sensing connects everyday context and activity to a range of online knowledge and information. The idea is to expand individual awareness of the world in ways which are congruent with our natural sensing abilities. Common sensing takes large knowledge bases and determines what information --- both momentary and persistent --- is relevant to the situation and activities of the user.

Common Sensing is the knowledge-based piece of the laboratory's initiative on Intimate Interfaces, combining context and background to present relevant information in natural non-disruptive ways.

Deep Background
This project is actively creating rich semantic knowledge bases to support both knowledge-aware applications and automatic machine understanding. These knowledge bases consist of both general world knowledge and specific knowledge about individuals, geography, and organizations.

Experiential Computing
Experiential computing is a new model for programming intelligent, adaptive, and autonomous systems. In experiential computing, most of the structure and logic of the computer's activity is driven by interactions between the current situation and descriptions of previous situations (experiences) stored in memory. Abstraction and generalization emerges mostly from interaction between current context and past experience.

Grounded Code
Grounded Code introduces common sense semantic knowledge into the development, deployment, and application of computer software. Most programs already include substantial amounts of common sense knowledge about themselves and their applications, hidden in comments, identifier names, and debugging code. The idea behind "grounded code" is to make this articulation systematic and tie it to deep background knowledge bases.

Impressionistic Browsers
Ken Haase

SearchEurope
Ken Haase
SearchEurope is a prototype interlingual search engine which uses interlingual concepts for describing web sites and other resources. As a first pass, it uses the DMOZ web directory, developed by Netscape/AOL and links that directory to the interlingual BRICO and XBRICO knowledge bases. These links allow users in different languages to browse interlingually with BRICO and then find websites related to the concepts the select from BRICO. SearchEurope will also allow interlingual annotation, so that individuals can annotate and categorize the web in their own languages while allowing users in other languages to use those annotations.

SemanticClay
Ken Haase

The Viscous Display
Lily Shirvanee

Visual Analogy
John Kelleher, Nick Hawes
The ability to judge similarity is one of the core attributes of human problem solving. Analogy extends the traditional remit of similarity to include the structure inherent in many situations. Taking the structure-mapping theory of analogy as a starting point, the Visual Analogy project investigates similarity and analogy, and their efficacy for solving different problems. In particular, the results of this research have been used to develop a system that solves visual analogy IQ test problems.