Mallory Selfridge

A computer model of child language learning

A computer program modelling a child between the ages of 1 and 2 years is described. This program is based on observations of the knowledge this child had at age 1, the comprehension abilities he had at age 2, and the language experiences he had between these ages. The computer program described begins at the age 1 level, is given similar language experiences, and uses inference and learning rules to acquire comprehension at the age 2 level.

A user-friendly high-level robot teaching system

Existing techniques for programming robots are either limited or require expert programmers. This paper describes an implemented robot system, TL, which is taught using a new technique which is easy to use yet produces powerful high-level plans. In this technique the user provides an example of the plan by moving the manipulator through the desired assembly. TL observes the assembly and infers the underlying plan. This paper describes TL, presents an example of it learning to assemble a simple electric switch, and discusses future directions for development.

A natural language interface to a robot assembly system

Natural language interaction with robots is an important goal since it promises access to robots by non-experts. Specifically, natural language interaction with robots would allow robots to be used easily and to be taught new skills. To produce such interaction requires, however, a choice among a variety of technologies in several areas. A working natural language interface to a robotic assembly system is described that allows a user to converse with the system about both vision and manipulation and to teach the system new vision knowledge and new assembly plans. The success of this approach suggest that its technologies are an appropriate choice for future real-world natural language interfaces to robot assembly systems.

Toward a natural language-based causal model acquisition system

Future expert systems for understanding physical mechanisms will probably employ causal models as the foundation of their expertise, and the problem of acquiring these causal models is important. This paper explores one possibility, that of acquiring causal models by understanding natural language explanations of these mechanisms. It identifies six different research issues in which understanding an explanation requires knowledge-based reasoning, and proposes approaches to these problems within an integrated natural language-based causal model acquisition system.

A cognitive model of the auditor's going-concern judgment

This article presents a cognitive model of the auditors going‐concern judgment, called the GCX model, that is based on the analysis of transcripts of interviews with expert auditors. It proposes a specific set of knowledge and reasoning skills to model the auditors performance of the going‐concern judgment. the GCX model has been implemented and tested in a computer program, called “GCX,” using data drawn from a realworld company, and its performance qualitatively matches that of expert auditors.

A robust natural language interface to a robot assembly system

Robots are increasingly being used in situations requiring them to exhibit flexible behavior. In such situations point-to-point motion sequences are inadequate. Instead, robots will probably use planning mechanisms, coupled with sensors of various kinds, to carry out their tasks. Such mechanisms allow robots to perform in situations where object position cannot be exactly specified in advance and in which unanticipated events can occur. One alternative is to teach the robot new plans. This paper describes a robot assembly system in which the robot is taught the steps in the new plan using natural language input.

A dynamic memory approach to case based reasoning: AI research on a Macintosh

An important open problem in the field of case-based reasoning is the retrieval of relevant cross-contextual cases. This paper describes an approach to the problem based on Schanks dynamic memory theory of human memory and an implementation of that approach. This system, called RUMPOLE, is implemented on a Macintosh SE and demonstrates the capability of the Macintosh for AI research.