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AIList Digest Volume 6 Issue 018
AIList Digest Friday, 29 Jan 1988 Volume 6 : Issue 18
Today's Topics:
Seminar - The Psychology of Everyday Things (SUNY) &
Combining O-O and DB Programming Languages? (Unisys) &
Learning Search Control Knowledge (AT&T) &
Thinkertools (BBN) &
BREAD, FRAPPE, and CAKE: Automated Deduction (SRI),
Conference - AAAIC88 Aerospace Applications of AI
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Date: Mon, 25 Jan 88 08:53:54 EST
From: rapaport@cs.Buffalo.EDU (William J. Rapaport)
Subject: Seminar - The Psychology of Everyday Things (SUNY)
STATE UNIVERSITY OF NEW YORK AT BUFFALO
The Steering Committee of the
GRADUATE STUDIES AND RESEARCH INITIATIVE IN
COGNITIVE AND LINGUISTIC SCIENCES
PRESENTS
DONALD A. NORMAN
Institute for Cognitive Science
University of California, San Diego
THE PSYCHOLOGY OF EVERYDAY THINGS
How do we manage the tasks of everyday life? The traditional answer is
that we engage in problem solving, planning, and thought. How do we
know what to do? Again, the traditional answer is that we learn, in
part through experience, in part through instruction. I suggest that
this view is misleading. Less planning and problem solving is required
than is commonly supposed. Many tasks need never be learned: the
proper behavior is obvious from the start. The problem space for most
everyday tasks is shallow or narrow, not wide and deep as the tradi-
tional approach suggests. The minimization of the problem space occurs
because natural and contrived properties of the environment combine to
constrain the set of possible actions. The effect is as if one had put
the knowledge required to do a thing on the thing itself: the knowledge
is in the world.
I show that seven stages are relevant to the performance of an action,
including three stages for execution of an act, three for evaluation,
and a goal stage. Consideration of the rule of each stage, along with
the principles of natural mappings and natural constraints, leads to a
set of psychological principles for design. Couple these principles
with the suggestion that most real tasks are shallow or narrow, and we
start to have a psychology of everyday things and everyday actions.
The talk itself is meant to be light and enjoyable. However, there are
profound implications for the type of theory one develops for simulating
cognitive computation. There are serious implications for massively
parallel structures (what we call Parallel Distributed Processing or
connectionist approaches), for memory storage and retrieval via descrip-
tions or coarse coding, and, in general, for a central role for pattern
matching, constraint satisficing, and nonsymbolic processing mechanisms
in human cognition. But the main implications of the work are for the
design of understandable and usable objects.
Monday, February 1, 1988
4:00 P.M.
Park 280, Amherst Campus
There will also be an informal evening discussion that evening at David
Zubin's house, 157 Highland St., at 8:00 P.M. Call Bill Rapaport (Com-
puter Science, (716) 636-3193, 3180) for further information.
------------------------------
Date: Tue, 26 Jan 88 11:44:22 EST
From: finin@PRC.Unisys.COM (Tim Finin)
Subject: Seminar - Combining O-O and DB Programming Languages?
(Unisys)
AI Seminar
UNISYS Knowledge Systems
Paoli Research Center
Paoli PA
Can We Combine Object-Oriented and Database Programming Languages?
Peter Buneman
Computer and Information Science
University of Pennsylvania
The inadequate expressive power of the relational data model for many
database representation tasks -- especially those that do not conform
to requirements of traditional data processing -- has led several
database systems developers to adopt an alternative "object-oriented"
approach to the representation of data. But if we do this, must we
necessarily sacrifice the the high-level languages and the
considerable implementation technology that have been developed for
relational databases? I shall argue that if we take a more liberal
attitude to what a relation is, we can generalize relational
languages, and even some of the ideas in relational database design,
to work for sets of objects.
A closely related problem is how we represent sets of objects as typed
values in a programming language. If we can find such a
representation, can data types be checked statically as in languages
like Pascal and Ada, or must we live with the difficulties and dangers
of run-time type checking? Some recent results by Atsushi Ohori
indicate that it is not only possible to do static type checking, but
that the types can be automatically inferred: the programmer does not
even have to declare the data types!
2:00 pm Wednesday, February 3, 1988
BIC Conference Room
Unisys Paloi Research Center
Route 252 and Central Ave.
Paoli PA 19311
-- non-Unisys visitors who are interested in attending should --
-- send email to finin@prc.unisys.com or call 215-648-7446 --
------------------------------
Date: Wed, 27 Jan 14:47:03 1988
From: dlm%research.att.com@RELAY.CS.NET
Subject: Seminar - Learning Search Control Knowledge (AT&T)
Learning Effective Search Control Knowledge: An Explanation-Based Approach
Steven Minton
Carnegie-Mellon University
Monday, February 1, 1988
10:30 AM
AT&T Bell Laboratories - Murray Hill 3C-436
In order to solve problems more effectively with accumulating
experience, a problem solver must be able to learn and exploit search
control knowledge. In this talk, I will discuss the application of
explanation-based learning (EBL) techniques for acquiring
domain-specific control knowledge. Although previous research has
demonstrated that EBL is a viable approach for acquiring control
knowledge, in practice EBL may not always generate useful control
knowledge. For control knowledge to be effective, the cumulative
benefits of applying the knowledge must outweigh the cumulative costs of
testing whether the knowledge is applicable. Generating effective
control knowledge may be difficult, as evidenced by the complexities
often encountered by human knowledge engineers. In general, control
knowledge cannot be indiscriminately added to a system; its costs and
benefits must be carefully taken into account.
To produce effective control knowledge, an explanation-based learner
must generate "good" explanations -- explanations that can be profitably
employed to control problem solving. In this talk, I will discuss the
utility of EBL and describe the PRODIGY system, a problem solver that
learns by searching for good explanations. I will also briefly describe
a formal model of EBL and a proof that PRODIGY's generalization
algorithm is correct.
Sponsor: Ron Brachman
------------------------------
Date: Thu 28 Jan 88 14:20:59-EST
From: Marc Vilain <MVILAIN@G.BBN.COM>
Subject: Seminar - Thinkertools (BBN)
BBN Science Development Program
AI/Education Seminar Series Lecture
THE THINKERTOOLS PROJECT:
CAUSAL MODELS, CONCEPTUAL CHANGE, AND SCIENCE EDUCATION
Barbara Y. White and Paul Horwitz
BBN Labs, Education Dept.
(BYWHITE@G.BBN.COM, PHORWITZ@G.BBN.COM)
BBN Labs
10 Moulton Street
2nd floor large conference room
10:30 am, Thursday February 4 (NOTE UNUSUAL DAY)
This talk will describe an approach to science education that enables
sixth graders to learn principles underlying Newtonian mechanics, and to
apply them in unfamiliar problem solving contexts. The students'
learning is centered around problem solving and experimentation within a
set of computer microworlds (i.e., interactive simulations). The
objective is for students to acquire gradually an increasingly
sophisticated causal model for reasoning about how forces affect the
motion of objects. To facilitate the evolution of such a mental model,
the microworlds incorporate a variety of linked alternative
representations for force and motion, and a set of game-like problem
solving activities designed to focus the students' inductive learning
processes. As part of the pedagogical approach, students formalize what
they learn into a set of laws, and critically examine these laws, using
criteria such as correctness, generality, and parsimony. They then go
on to apply their laws to a variety of real world problems. The
approach synthesizes the learning of the subject matter with learning
about the nature of scientific knowledge -- what are scientific laws,
how do they evolve, and why are they useful? Instructional trials found
that the curriculum is equally effective for males and females, and for
students of different ability levels. Further, sixth graders taught
with this approach do better on classic force and motion problems than
high school students taught using traditional methods.
------------------------------
Date: Thu, 28 Jan 88 12:23:33 PST
From: Amy Lansky <lansky@venice.ai.sri.com>
Subject: Seminar - BREAD, FRAPPE, and CAKE: Automated Deduction (SRI)
BREAD, FRAPPE, AND CAKE:
THE GOURMET'S GUIDE TO AUTOMATED DEDUCTION
Yishai A. Feldman (YISHAI@AI.AI.MIT.EDU)
AI Laboratory, MIT
11:00 AM, WEDNESDAY, February 3
SRI International, Building E, Room EJ228
Cake is the knowledge representation and reasoning system developed as
part of the Programmer's Apprentice project. Cake can be thought of
as an active database, which performs quick and shallow deduction
automatically; it supports both forward-chaining and backward-chaining
reasoning. The Cake system has a layered architecture: the kernel of
the system, called Bread (for Basic REAsoning Device), is a
truth-maintenance system with equality and demons. Built on top of
this is Frappe (for FRAmes in a ProPositional Engine), which
implements a typed logic with special-purpose decision procedures for
various algebraic properties of operators (such as commutativity and
associativity), sets, partial functions, and structured objects
(frames). Only the topmost layer of Cake, which implements the Plan
Calculus, is specific to reasoning about programs. This talk will
describe the architecture and features of Bread, Frappe, and Cake,
including a transcript of a demonstration session. This is joint work
with Charles Rich.
VISITORS: Please arrive 5 minutes early so that you can be escorted up
from the E-building receptionist's desk. Thanks!
------------------------------
Date: 26 Jan 88 16:54:00 EST
From: "ETD2::WILSONJ" <wilsonj%etd2.decnet@afwal-aaa.arpa>
Reply-to: "ETD2::WILSONJ" <wilsonj%etd2.decnet@afwal-aaa.arpa>
Subject: Conference - AAAIC88 Aerospace Applications of AI
AAAIC88 CALL FOR PAPERS
AEROSPACE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE 1988
With Neural Networks Aerospace Applications
Special Interest Sessions
Stouffer's Hotel, Dayton, OH, October 25-27, 1988
Particulars - Tutorials will be held on 24 Oct 88. Workshops will be held on
28 Oct 88. There will be exhibits by AI companies and related industries as
well as product familiarization sessions. There will be up to 18 technical
sessions in 5 half-day periods, luncheon speakers and a banquet.
The 4th Aerospace Applications of Artificial Intelligence Conference will
investigate a wide range of topics with heavy emphasis this year on neural
network applications in aerospace. Topic areas for which timely, original,
technical papers are solicited include:
Integrating Neural Networks and Knowledge Processing with Neural Nets
Expert Systems Robotics
Neural Networks and Signal Processing Data Fusion/Sensor Fusion
Machine Learning, Cognition & the Combinatorial Optimization for
Cockpit Scheduling and Resource Control
Machine Vision & Avionics Applications Natural Language Recognition and
Neural Networks and Man-Machine Synthesis
Interface Issues Self-Organization in Avionics
Neural Network Development Tools Applied Adaptive-Resonance
Applied Biological Models Cooperative and Competitive Network
Parallel Processing & Neural Networks Dynamics in Aerospace
Automatic Target Recognition Learning Theory and Techniques
Back Propagation with Momentum, Simulation and Implementation of
Shared Weights or Recurrent Neural Networks
Network Architectures Technology - Microchips, Optics, etc.
Expert System Development Tools Applications of Expert Systems in
Aerospace Scheduling Manufacturing
Operational and Maintenance Issues Design Automation
Using Expert Systems Data Management
Real Time Expert Systems Acquisition Management
Knowledge Base Simulation Verification and Validation of ES
Advanced Problem Solving Techniques Diagnostics and Fault Isolation
ABSTRACT DEADLINE : 26 Feb 88
Authors are invited to submit abstracts of 500 words in any of the above topic
areas. Please avoid acronyms or abbreviations in the title of the paper. A
short biographical sketch of the author(s) to include citizenship, mailing
address and telephone number must be included with the abstract. Final
manuscripts for papers are due 19 Aug 88.
James R. Johnson
Send abstracts to: AFWAL/AAOR
WPAFB, OH 45433
Sponsored by Dayton SIGART and the Association of Computing Machinery.
