Current subprojects include applications of the program Gerring.

Palladio is an experimental system for designing very lame scale integrated (VLSI) circuits.

Scientific Dateline Heuristic Programming Project May 1983 Report No. HPP-83-1 5 (Revised June 1983)

OMEN is an object-oriented programming system designed for use in a FRANI LISP or other similar programming environment. OMEN stands for OHiccr MANIPULATION ENVIRONNIFN r, and consists of a set of functions to be loaded on top of a!ASP system running MRS. The user can the use the functions provided by OMEN to create classes of objects, instances of those classes, and functions that operate on those objects, and to send messages to those objects. OMEN is similar in design and operation to the flavors system of Lisp Machine lisp and the LOOPS system for the Xerox Dolphin. OMEN was originally designed as a programming eny ironmcnt for an objectoriemed graphics system, but the system should be useful for many different applications. OMEN is not a programming language. It is a way of abstracting the data structures a program must use and the functions that operate on those data structures.

Overview of GLISP GLISP is a LISP-based language which provides high-level language features not found in ordinary LISP. The GLISP language is implemented by means of a compiler which accepts GLISP as input and produces ordinary LISP as output; this output can be further compiled to machine code by the LISP compiler. The goal of GLISP is to allow structured objects to be reft-trenced in a convenient, succinct language, and to allow the structures of objects to be changed without changing the code which references the objects. The syntax of many GLISP constructs is English-like; much of the power and brevity of GLISP derive from the compiler features necessary to support the relatively informal.

This article is concerned with the general question of how to represent biological knowledge in computers such that it may be used in multiple problem solving tasks. In particular, I present a model of a bacterial gene regulation system that is used by a program that simulates gene regulation experiments, and by a second program that formulates hypotheses to account for errors in predicted experiment outcomes. This article focuses on the issues of representation and simulation; for more information on the hypothesis formation task see (Karp, 1989; Karp, 1990). The bacterial gene regulation system of interest is the tryptophan (trp) operon of E. coli (Yanofsky, 1981). The genes that it contains code for enzymes that synthesize the amino acid tryptophan.

An early expression of this difficulty is the socalled might be stated that Socrates's birthplace was Athens.

We present ongoing research to develop novel option discovery methods for complex domains that are represented as Object-Oriented Markov Decision Processes (OO-MDPs) (Diuk, Cohen, and Littman, 2008). We describe Portable Multi-policy Option Discovery for Automated Learning (P-MODAL), an initial framework that extends Pickett and Barto’s (2002) PolicyBlocks approach to OO-MDPs. We also discuss future work that will use additional representations and techniques to handle scalability and learning challenges.

Being able to quickly and naturally teach robots new knowledge is critical for many future open-world human-robot interaction scenarios. In this paper we present a novel approach to using natural language context for one-shot learning of visual objects, where the robot is immediately able to recognize the described object. We describe the architectural components and demonstrate the proposed approach on a robotic platform in a proof-of-concept evaluation.

RUM (Reasoning with Uncertainty Module), is an integrated software tool based on a KEE, a frame system implemented in an object oriented language. RUM's architecture is composed of three layers: representation, inference, and control. The representation layer is based on frame-like data structures that capture the uncertainty information used in the inference layer and the uncertainty meta-information used in the control layer. The inference layer provides a selection of five T-norm based uncertainty calculi with which to perform the intersection, detachment, union, and pooling of information. The control layer uses the meta-information to select the appropriate calculus for each context and to resolve eventual ignorance or conflict in the information. This layer also provides a context mechanism that allows the system to focus on the relevant portion of the knowledge base, and an uncertain-belief revision system that incrementally updates the certainty values of well-formed formulae (wffs) in an acyclic directed deduction graph. RUM has been tested and validated in a sequence of experiments in both naval and aerial situation assessment (SA), consisting of correlating reports and tracks, locating and classifying platforms, and identifying intents and threats. An example of naval situation assessment is illustrated. The testbed environment for developing these experiments has been provided by LOTTA, a symbolic simulator implemented in Flavors. This simulator maintains time-varying situations in a multi-player antagonistic game where players must make decisions in light of uncertain and incomplete data. RUM has been used to assist one of the LOTTA players to perform the SA task.