206839040
A Look at TPS
Certain aspects of the theorem proving system TPS are described. Type theory with λ-abstraction has been chosen as the logical language of TPS so that statements from many fields of mathematics and other disciplines can be expressed in terms accessible to the system.
14782174
Uniform Proofs as a Foundation for Logic Programming
Abstract Miller, D., G. Nadathur, F. Pfenning and A. Scedrov, Uniform proofs as a foundation for logic programming, Annals of Pure and Applied Logic 51 (1991) 125–157. A proof-theoretic characterization of logical languages that form suitable bases for Prolog-like programming languages is provided. This characterization is based on the principle that the declarative meaning of a logic program, provided by provability in a logical system, should coincide with its operational meaning, provided by interpreting logical connectives as simple and fixed search instructions. The operational semantics is formalized by the identification of a class of cut-free sequent proofs called uniform proofs. A uniform proof is one that can be found by a goal-directed search that respects the interpretation of the logical connectives as search instructions. The concept of a uniform proof is used to define the notion of an abstract logic programming language, and it is shown that first-order and higher-order Horn clauses with classical provability are examples of such a language. Horn clauses are then generalized to hereditary Harrop formulas and it is shown that first-order and higher-order versions of this new class of formulas are also abstract logic programming languages if the inference rules are those of either intuitionistic or minimal logic. The programming language significance of the various generalizations to first-order Horn clauses is briefly discussed.
14544430
Specifying Theorem Provers in a Higher-Order Logic Programming Language
Since logic programming systems directly implement search and unification and since these operations are essential for the implementation of most theorem provers, logic programming languages should make ideal implementation languages for theorem provers. We shall argue that this is indeed the case if the logic programming language is extended in several ways. We present an extended logic programming language where first-order terms are replaced with simply-typed λ-terms, higher-order unification replaces first-order unification, and implication and universal quantification are allowed in queries and the bodies of clauses. This language naturally specifies inference rules for various proof systems. The primitive search operations required to search for proofs generally have very simple implementations using the logical connectives of this extended logic programming language. Higher-order unification, which provides sophisticated pattern matching on formulas and proofs, can be used to determine when and at what instance an inference rule can be employed in the search for a proof. Tactics and tacticals, which provide a framework for high-level control over search, can also be directly implemented in this extended language. The theorem provers presented in this paper have been implemented in the higher-order logic programming language λProlog.
5180180
Logic programming in a fragment of intuitionistic linear logic
The intuitionistic notion of context is refined by using a fragment of J.-Y. Girard's (Theor. Comput. Sci., vol.50, p.1-102, 1987) linear logic that includes additive and multiplicative conjunction, linear implication, universal quantification, the of course exponential, and the constants for the empty context and for the erasing contexts. It is shown that the logic has a goal-directed interpretation. It is also shown that the nondeterminism that results from the need to split contexts in order to prove a multiplicative conjunction can be handled by viewing proof search as a process that takes a context, consumes part of it, and returns the rest (to be consumed elsewhere). Examples taken from theorem proving, natural language parsing, and database programming are presented: each example requires a linear, rather than intuitionistic, notion of context to be modeled adequately.<<ETX>>
10134130
A Congruence Format for Name-passing Calculi
We define and use a SOS-based framework to specify the transition systems of calculi with name-passing properties. This setting uses proof-theoretic tools to take care of some of the difficulties specific to name-binding and make them easier to handle in proofs. The contribution of this paper is the presentation of a format that ensures that open bisimilarity is a congruence for calculi specified within this framework, extending the well-known tyft/tyxt format to the case of name-binding and name-passing. We apply this result to the @p-calculus in both its late and early semantics.
5278164
A Logic Programming Approach to Manipulating Formulas and Programs
By permitting function and predicate symbols to be variables and by replacing first-order terms by simply typed $lambda$-terms, a higher-order extension to first-order Horn clauses can be described. To be complete in principle, a logic programming language based on this extension must incorporate higher-order unification. Although this is a more complex operation than first-order unification, its availability as a primitive makes certain kinds of programming tasks easier. We illustrate this aspect in the paper by presenting some programs in a higher-order version of Prolog called $\lambda$Prolog that manipulate objects, such as formulas and programs, whose representation requires structures containing abstractions or bound variables. Specifically, we show (a) how a simple natural deduction theorem prover may be implemented in $\lambda$Prolog, and (b) how some simple program transformers for a functional programming language may be written in this language. These programs exploit the availability of $\lambda$-conversion and higher-order unification in $\lambda$Prolog to provide elegant solutions to several awkward problems inherent in these tasks. We contend that programs which perform similar tasks but which are written in languages without these features would, at least, be much less perspicuous.
16341198
Global Symbolic Maps from Local Navigation
In order to navigate autonomously, most robot systems are provided with some sort of global terrain map. To make storage practical, these maps usually have a high-level symbolic representation of the terrain. The robot's symbolic map is then used to plan a local path. This paper describes a system which uses the reverse (and perhaps more natural) process. This system processes local sensor data in such a way as to allow efficient, reactive local navigation. A byproduct of this navigation process is an abstraction of the terrain information which forms a global symbolic terrain map of the terrain through which the robot has passed. Since this map is in the same format as that used by the local navigation system, the map is easy for the system to use, augment, or correct. Compared with the data from which the maps are created, the maps are very space efficient, and can be modified, or used for navigation in real-time. Experiments with this system, both in simulation, and with a real robot operating in natural terrain, are described.
43128338
Experiences with an architecture for intelligent, reactive agents
This paper describes an implementation of the 3T robot architecture which has been under development for the last eight years. The architecture uses three levels of abstraction and description languages which are compatible between levels. The makeup of the architecture helps to coordinate planful activities with real-time behaviours for dealing with dynamic environments. In recent years, other architectures have been created with similar attributes but two features distinguish the 3T architecture: (1) a variety of useful software tools have been created to help implement this architecture on multiple real robots; and (2) this architecture, or parts of it, have been implemented on a variety of very different robot systems using different processors, operating systems, effectors and sensor suites.
6001799
Limit to the Bit-Rate Capacity of Electrical Interconnects from the Aspect Ratio of the System Architecture
We show that there is a limit to the total number of bits per second,B, of information that can flow in a simple digital electrical interconnection that is set only by the ratio of the lengthlof the interconnection to the total cross-sectional dimensionAof the interconnect wiring?the “aspect ratio” of the interconnection. This limit is largely independent of the details of the design of the electrical lines. The limit is approximatelyB~BoA/l2bits/s, withBo~ 1015(bit/s) for high-performance strip lines and cables, ~1016for small on-chip lines, and ~1017?1018for equalized lines. Because the limit is scale-invariant, neither growing nor shrinking the system substantially changes the limit. Exceeding this limit requires techniques such as repeatering, coding, and multilevel modulation. Such a limit will become a problem as machines approach Tb/s information bandwidths. The limit will particularly affect architectures in which one processor must talk reasonably directly with many others. We argue that optical interconnects can solve this problem since they avoid the resistive loss physics that gives this limit.
17425751
A Mixture of Experts Classifier with Learning Based on Both Labelled and Unlabelled Data
We address statistical classifier design given a mixed training set consisting of a small labelled feature set and a (generally larger) set of unlabelled features. This situation arises, e.g., for medical images, where although training features may be plentiful, expensive expertise is required to extract their class labels. We propose a classifier structure and learning algorithm that make effective use of unlabelled data to improve performance. The learning is based on maximization of the total data likelihood, i.e. over both the labelled and unlabelled data subsets. Two distinct EM learning algorithms are proposed, differing in the EM formalism applied for unlabelled data. The classifier, based on a joint probability model for features and labels, is a "mixture of experts" structure that is equivalent to the radial basis function (RBF) classifier, but unlike RBFs, is amenable to likelihood-based training. The scope of application for the new method is greatly extended by the observation that test data, or any new data to classify, is in fact additional, unlabelled data - thus, a combined learning/classification operation - much akin to what is done in image segmentation - can be invoked whenever there is new data to classify. Experiments with data sets from the UC Irvine database demonstrate that the new learning algorithms and structure achieve substantial performance gains over alternative approaches.
16186126
Representing and Reasoning with Operational Semantics
The operational semantics of programming and specification languages is often presented via inference rules and these can generally be mapped into logic programming-like clauses. Such logical encodings of operational semantics can be surprisingly declarative if one uses logics that directly account for term-level bindings and for resources, such as are found in linear logic. Traditional theorem proving techniques, such as unification and backtracking search, can then be applied to animate operational semantic specifications. Of course, one wishes to go a step further than animation: using logic to encode computation should facilitate formal reasoning directly with semantic specifications. We outline an approach to reasoning about logic specifications that involves viewing logic specifications as theories in an object-logic and then using a meta-logic to reason about properties of those object-logic theories. We motivate the principal design goals of a particular meta-logic that has been built for that purpose.