Sound and Complete Qualitative Simulation Needs “Quantitative” Filtering

The AI methodology of qualitative reasoning furnishes useful tools to scientists and engineers who need to deal with incomplete system knowledge during design, analysis, or diagnosis tasks. Qualitative simulators have a theoretical soundness guarantee; they cannot overlook any concrete equation implied by their input. On the other hand, the basic qualitative simulation algorithms have been shown to suffer from the incompleteness problem; they may allow non-solutions of the input equation to appear in their output. The question of whether a simulator with purely qualitative input which never predicts spurious behaviors can ever be achieved by adding new filters to the existing algorithm has remained unanswered. In this paper, we show that, if such a sound and complete simulator exists, it will have to be able to handle numerical distinctions with such a high precision that it must contain a component that would better be called a quantitative, rather than qualitative reasoner. This is due to the ability of the pure qualitative format to allow the exact representation of the members of a rich set of numbers.     

n-Dimensional Processor Arrays with Optical dBuses

dBus-array(k,n) is an n-dimensional processor array of kⁿ nodes connected via k^n–1 dBuses. A dBus is a unidirectional bus which receives signals from a set of k nodes (input set), and transmits signals to a different set of k nodes (output set). Two optical implementations of the dBus-array(k,n) are discussed. One implementation uses the wavelength division multiplexing as in the wavelength division multiple access channel hypercube WMCH [7]. WMCH(k,n) and dBus-array(k,n) have the same diameter and about the same average internode distance, while the dBus-array requires only one tunable transmitter/receiver per node, compared to n tunable transmitters/receivers per node for the WMCH. The other implementation uses one fixed-wavelength transmitter/receiver per node and the dilated slipped banyan switching network (DSB) [17] to combine time division and wavelength division multiplexing.     

Prediction Markets as Decision Support Systems

Valuations from prediction markets reveal expectations about the likelihood of events. Conditional prediction markets reveal expectations conditional on other events occurring. For example, in 1996, the Iowa Electronic Markets (IEM) ran markets to predict the chances that different candidates would become the Republican Presidential nominee. Other concurrent IEM markets predicted the vote shares that each party would receive conditional on the Republican nominee chosen. Here, using these markets as examples, we show how such markets could be used for decision support. In this example, Republicans could have inferred that Dole was a weak candidate and that his nomination would result in a Clinton victory. This is only one example of the widespread potential for using specific decision support markets.     

A New Class of Depth-Size Optimal Parallel Prefix Circuits

Given n values x₁, x₂, ... ,x_n and an associative binary operation o, the prefix problem is to compute x₁ox₂o··· ox_i, 1in. Many combinational circuits for solving the prefix problem, called prefix circuits, have been designed. It has been proved that the size s(D(n)) and the depth d(D(n)) of an n-input prefix circuit D(n) satisfy the inequality d(D(n))+s(D(n))2n–2; thus, a prefix circuit is depth-size optimal if d(D(n))+s(D(n))=2n–2. In this paper, we construct a new depth-size optimal prefix circuit SL(n). In addition, we can build depth-size optimal prefix circuits whose depth can be any integer between d(SL(n)) and n–1. SL(n) has the same maximum fan-out lgn+1 as Snir's SN(n), but the depth of SL(n) is smaller; thus, SL(n) is faster. Compared with another optimal prefix circuit LYD(n), d(LYD(n))+2d(SL(n))d(LYD(n)). However, LYD(n) may have a fan-out of at most 2 lgn–2, and the fan-out of LYD(n) is greater than that of SL(n) for almost all n12. Because an operation node with greater fan-out occupies more chip area and is slower in VLSI implementation, in most cases, SL(n) needs less area and may be faster than LYD(n). Moreover, it is much easier to design SL(n) than LYD(n).     

Complexity and computability of solutions to linear programming systems

Through key examples and constructs, exact and approximate, complexity, computability, and solution of linear programming systems are reexamined in the light of Khachian's new notion of (approximate) solution. Algorithms, basic theorems, and alternate representations are reviewed. It is shown that the Klee-Minty example hasnever been exponential for (exact) adjacent extreme point algorithms and that the Balinski-Gomory (exact) algorithm continues to be polynomial in cases where (approximate) ellipsoidal centered-cutoff algorithms (Levin, Shor, Khachian, Gacs-Lovasz) are exponential. By model approximation, both the Klee-Minty and the new J. Clausen examples are shown to be trivial (explicitly solvable) interval programming problems. A new notion of computable (approximate) solution is proposed together with ana priori regularization for linear programming systems. New polyhedral constraint contraction algorithms are proposed for approximate solution and the relevance of interval programming for good starts or exact solution is brought forth. It is concluded from all this that the imposed problem ignorance of past complexity research is deleterious to research progress on computability or efficiency of computation.This research was partly supported by Project NR047-071, ONR Contract N00014-80-C-0242, and Project NR047-021, ONR Contract N00014-75-C-0569, with the Center for Cybernetic Studies, The University of Texas at Austin.     

An Efficient Fixed Parameter Tractable Algorithm for 1-Sided Crossing Minimization

We give an O(^k · n²) fixed parameter tractable algorithm for the 1-Sided Crossing Minimization. The constant in the running time is the golden ratio = (1+5)/2 1.618. The constant k is the parameter of the problem: the number of allowed edge crossings.     

A model implementation for protective domains

A combination hardware/software mechanism is presented which supports very general capabilities for the protection of and controlled access to sharable information structures. It is defined through symbolic algorithms in terms of the dedicated model hardware. The model centers on two key concepts, that of thetenant, who is a storage holding entity, and that of thedomain, which is an information accessing entity. The domain, defined as a capsular collection of mutually accessible information structures having a single common external protective interface, is an integral part of the hardware logic. It is contended that the definition of a mechanism to enforce access authorizations must include an underlying philosophy specifying the conditions under which such access authorizations may be granted. Such a philosophy is suggested. It is based on theprinciple of ownership according to which any area of storage is at all times held by a single tenant who has the exclusive right to grant/revoke access privileges to his proprietary information structures, i.e., information residing in proprietary storage.This is an extensively revised version of a paper presented under the title A Computer System Model for Controlled Sharing of Information at ONLINE72, September 1972, Brunel University, Uxbridge, Middlesex, England. Republished by permission of ONLINE72.Work reported in this paper is of a theoretical nature and may not be construed to imply any product commitment by the Digital Equipment Corporation, Maynard, Massachusetts.     

MAAN: A Multi-Attribute Addressable Network for Grid Information Services

Recent structured Peer-to-Peer (P2P) systems such as Distributed Hash Tables (DHTs) offer scalable key-based lookup for distributed resources. However, they cannot be simply applied to grid information services because grid resources need to be registered and searched using multiple attributes. This paper proposes a Multi-Attribute Addressable Network (MAAN) that extends Chord to support multi-attribute and range queries. MAAN addresses range queries by mapping attribute values to the Chord identifier space via uniform locality preserving hashing. It uses an iterative or single attribute dominated query routing algorithm to resolve multi-attribute based queries. Each node in MAAN only has O(logN) neighbors for N nodes. The number of routing hops to resolve a multi-attribute range query is O(logN+N×s_min), where s_min is the minimum range selectivity on all attributes. When s_min=, it is logarithmic to the number of nodes, which is scalable to a large number of nodes and attributes. We also measured the performance of our MAAN implementation and the experimental results are consistent with our theoretical analysis.     

Circuit-Switched Broadcasting in Multi-Port Multi-Dimensional Torus Networks

The one-to-all broadcast is the most primary collective communication pattern in a multicomputer network. This paper studies this problem in a circuit-switched torus with -port capability, where a node can simultaneously send and receive messages at one time. This is a generalization of the one-port and all-port models. We show how to efficiently perform broadcast in tori of any dimension, any size, square or nonsquare, using near optimal numbers of steps. The main techniques used are: (i) a span-by-dimension approach, which makes our solution scalable to torus dimensions, and (ii) a squeeze-then-expand approach, which makes possible solving the difficult cases where tori are non-square. Existing results, as compared to ours, can only solve very restricted sizes or dimensions of tori, or use more numbers of steps.     

Least Squares Policy Evaluation Algorithms with Linear Function Approximation

We consider policy evaluation algorithms within the context of infinite-horizon dynamic programming problems with discounted cost. We focus on discrete-time dynamic systems with a large number of states, and we discuss two methods, which use simulation, temporal differences, and linear cost function approximation. The first method is a new gradient-like algorithm involving least-squares subproblems and a diminishing stepsize, which is based on the -policy iteration method of Bertsekas and Ioffe. The second method is the LSTD() algorithm recently proposed by Boyan, which for =0 coincides with the linear least-squares temporal-difference algorithm of Bradtke and Barto. At present, there is only a convergence result by Bradtke and Barto for the LSTD(0) algorithm. Here, we strengthen this result by showing the convergence of LSTD(), with probability 1, for every [0, 1].     

Beating the Logarithmic Lower Bound: Randomized Preemptive Disjoint Paths and Call Control Algorithms

We consider the maximum disjoint paths problem and its generalization, the call control problem, in the on-line setting. In the maximum disjoint paths problem, we are given a sequence of connection requests for some communication network. Each request consists of a pair of nodes, that wish to communicate over a path in the network. The request has to be immediately connected or rejected, and the goal is to maximize the number of connected pairs, such that no two paths share an edge. In the call control problem, each request has an additional bandwidth specification, and the goal is to maximize the total bandwidth of the connected pairs (throughput), while satisfying the bandwidth constraints (assuming each edge has unit capacity). These classical problems are central in routing and admission control in high speed networks and in optical networks.We present the first known constant-competitive algorithms for both problems on the line. This settles an open problem of Garay et al. and of Leonardi. Moreover, to the best of our knowledge, all previous algorithms for any of these problems, are (logn)-competitive, where n is the number of vertices in the network (and obviously noncompetitive for the continuous line). Our algorithms are randomized and preemptive. Our results should be contrasted with the (logn) lower bounds for deterministic preemptive algorithms of Garay et al. and the (logn) lower bounds for randomized non-preemptive algorithms of Lipton and Tomkins and Awerbuch et al. Interestingly, nonconstant lower bounds were proved by Canetti and Irani for randomized preemptive algorithms for related problems but not for these exact problems.     

Unitary immersions of nonlinear systems

In this paper, we define what we call a unitary immersion of a nonlinear system. We observe that, for classical Hamiltonian systems, this notion contains, in some sense, the concept of quantization. We restrict our attention to degree-zero unitary immersions, where all observation functions must be represented by operators of the type multiplication by a function. We show that the problem of classifying such degree-zero unitary immersions of a given nonlinear system is not obvious. In some cases, we solve this problem.Chargé de Recherche au CNRS.Maître de Conférences.     

MEG-Eliminations

The basic disadvantage of the algorithms of EG-eliminations [1] is that the degrees of elements of the polynomial matrix being used and the coefficients of these elements grow. The progress in overcoming this difficulty is associated with the use of modular approaches. The paper describes modularization of EG-eliminations and a modified elimination scheme based on that modularization (quasi-modular algorithm).     

The dynamic clusters method in nonhierarchical clustering

Given a finite setE R ⁿ, the problem is to find clusters (or subsets of similar points inE) and at the same time to find the most typical elements of this set. An original mathematical formulation is given to the problem. The proposed algorithm operates on groups of points, called samplings (samplings may be called multiple centers or cores); these samplings adapt and evolve into interesting clusters. Compared with other clustering algorithms, this algorithm requires less machine time and storage. We provide some propositions about nonprobabilistic convergence and a sufficient condition which ensures the decrease of the criterion. Some computational experiments are presented.     

Unification in partially commutative semigroups

Unification algorithms have been constructed for semigroups and commutative semigroups. This paper considers the intermediate case of partially commutative semigroups. We introduce classesN and of such semigroups and justify their use. We present an equation-solving algorithm for any member of the classN. This algorithm is relative to having an algorithm to determine all non-negative solutions of a certain class of diophantine equations of degree 2 which we call -equations. The difficulties arising when attempting to solve equations in members of the class are discussed, and we present arguments that strongly suggest that unification in these semigroups is undecidable.     

The discovery of the artificial. Some protocybernetic developments 1930–1940

In this paper I start from a definition of culture of the artificial which might be stated by referring to the background of philosophical, methodological, pragmatical assumptions which characterizes the development of the information processing analysis of mental processes and of some trends in contemporary cognitive science: in a word, the development of AI as a candidate science of mind. The aim of this paper is to show how (with which plausibility and limitations) the discovery of the mentioned background might be dated back to a period preceding the cybernetic era, the decade 1930–1940 at least. Therefore a somewhat detailed analysis of Hull's robot approach is given, as well as of some of its independent and future developments.     

From Conditional Events to Conditional Measures: A New Axiomatic Approach

Our starting point is a definition of conditional event EH which differs from many seemingly similar ones adopted in the relevant literature since 1935, starting with de Finetti. In fact, if we do not assign the same third value u (undetermined) to all conditional events, but make it depend on EH, it turns out that this function t(EH) can be taken as a general conditional uncertainty measure, and we get (through a suitable – in a sense, compulsory – choice of the relevant operations among conditional events) the natural axioms for many different (besides probability) conditional measures.     

Backtracking Problem in the Traversal of an Unknown Directed Graph by a Finite Robot

A covering path in a directed graph is a path passing through all vertices and arcs of the graph, with each arc being traversed only in the direction of its orientation. A covering path exists for any initial vertex only if the graph is strongly connected. The traversal of an unknown graph implies that the topology of the graph is not a priori known, and we learn it only in the course of traversing the graph. This is similar to the problem of traversing a maze by a robot in the case where the plan of the maze is not available. If the robot is a general-purpose computer without any limitations on the number of its states, then traversal algorithms with the estimate O(nm) are known, where n is the number of vertices and m is the number of arcs. If the number of states is finite, then this robot is a finite automaton. Such a robot is an analogue of the Turing machine, where the tape is replaced by a graph and the cells are assigned to the graph vertices and arcs. The selection of the arc that has not been traversed yet among those originating from the current vertex is determined by the order of the outgoing arcs, which is a priori specified for each vertex. The best known traversal algorithms for a finite robot are based on constructing the output directed spanning tree of the graph with the root at the initial vertex and traversing it with the aim to find all untraversed arcs. In doing so, we face the backtracking problem, which consists in searching for all vertices of the tree in the order inverse to their natural partial ordering, i.e., from the leaves to the root. Therefore, the upper estimate of the algorithms is different from the optimal estimate O(nm) by the number of steps required for the backtracking along the outgoing tree. The best known estimate O(nm + n ²loglogn) has been suggested by the author in the previous paper [1]. In this paper, a finite robot is suggested that performs a backtracking with the estimate O(n ²log*(n)). The function log* is defined as an integer solution of the inequality 1 log₂ ^log*(n) < 2, where log ^t = log º log º ... º log (the superposition º is applied t – 1 times) is the tth compositional degree of the logarithm. The estimate O(nm + n ²log*(n)) for the covering path length is valid for any strongly connected graph for a certain (unfortunately, not arbitrary) order of the outgoing arcs. Interestingly, such an order of the arcs can be marked by symbols of the finite robot traversing the graph. Hence, there exists a robot that traverses the graph twice: first traversal with the estimate O(nm + n ²loglogn) and the second traversal with the estimate O(nm + n ²log*(n)).     

Proof Reflection in Coq

We formalize natural deduction for first-order logic in the proof assistant Coq, using de Bruijn indices for variable binding. The main judgment we model is of the form d[:], stating that d is a proof term of formula under hypotheses it can be viewed as a typing relation by the Curry–Howard isomorphism. This relation is proved sound with respect to Coq's native logic and is amenable to the manipulation of formulas and of derivations. As an illustration, we define a reduction relation on proof terms with permutative conversions and prove the property of subject reduction.