id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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cs/9501101 | Solving Multiclass Learning Problems via Error-Correcting Output Codes | cs.AI | Multiclass learning problems involve finding a definition for an unknown
function f(x) whose range is a discrete set containing k > 2 values (i.e., k
``classes''). The definition is acquired by studying collections of training
examples of the form [x_i, f (x_i)]. Existing approaches to multiclass learning
problems ... |
cs/9501102 | A Domain-Independent Algorithm for Plan Adaptation | cs.AI | The paradigms of transformational planning, case-based planning, and plan
debugging all involve a process known as plan adaptation - modifying or
repairing an old plan so it solves a new problem. In this paper we provide a
domain-independent algorithm for plan adaptation, demonstrate that it is sound,
complete, and s... |
cs/9501103 | Truncating Temporal Differences: On the Efficient Implementation of
TD(lambda) for Reinforcement Learning | cs.AI | Temporal difference (TD) methods constitute a class of methods for learning
predictions in multi-step prediction problems, parameterized by a recency
factor lambda. Currently the most important application of these methods is to
temporal credit assignment in reinforcement learning. Well known reinforcement
learning a... |
cs/9503101 | On the Informativeness of the DNA Promoter Sequences Domain Theory | cs.AI q-bio | The DNA promoter sequences domain theory and database have become popular for
testing systems that integrate empirical and analytical learning. This note
reports a simple change and reinterpretation of the domain theory in terms of
M-of-N concepts, involving no learning, that results in an accuracy of 93.4% on
the 10... |
cs/9503102 | Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic
Decision Tree Induction Algorithm | cs.AI | This paper introduces ICET, a new algorithm for cost-sensitive
classification. ICET uses a genetic algorithm to evolve a population of biases
for a decision tree induction algorithm. The fitness function of the genetic
algorithm is the average cost of classification when using the decision tree,
including both the co... |
cs/9504101 | Rerepresenting and Restructuring Domain Theories: A Constructive
Induction Approach | cs.AI | Theory revision integrates inductive learning and background knowledge by
combining training examples with a coarse domain theory to produce a more
accurate theory. There are two challenges that theory revision and other
theory-guided systems face. First, a representation language appropriate for
the initial theory m... |
cs/9505101 | Using Pivot Consistency to Decompose and Solve Functional CSPs | cs.AI | Many studies have been carried out in order to increase the search efficiency
of constraint satisfaction problems; among them, some make use of structural
properties of the constraint network; others take into account semantic
properties of the constraints, generally assuming that all the constraints
possess the give... |
cs/9505102 | Adaptive Load Balancing: A Study in Multi-Agent Learning | cs.AI | We study the process of multi-agent reinforcement learning in the context of
load balancing in a distributed system, without use of either central
coordination or explicit communication. We first define a precise framework in
which to study adaptive load balancing, important features of which are its
stochastic natur... |
cs/9505103 | Provably Bounded-Optimal Agents | cs.AI | Since its inception, artificial intelligence has relied upon a theoretical
foundation centered around perfect rationality as the desired property of
intelligent systems. We argue, as others have done, that this foundation is
inadequate because it imposes fundamentally unsatisfiable requirements. As a
result, there ha... |
cs/9505104 | Pac-Learning Recursive Logic Programs: Efficient Algorithms | cs.AI | We present algorithms that learn certain classes of function-free recursive
logic programs in polynomial time from equivalence queries. In particular, we
show that a single k-ary recursive constant-depth determinate clause is
learnable. Two-clause programs consisting of one learnable recursive clause and
one constant... |
cs/9505105 | Pac-learning Recursive Logic Programs: Negative Results | cs.AI | In a companion paper it was shown that the class of constant-depth
determinate k-ary recursive clauses is efficiently learnable. In this paper we
present negative results showing that any natural generalization of this class
is hard to learn in Valiant's model of pac-learnability. In particular, we show
that the foll... |
cs/9506101 | FLECS: Planning with a Flexible Commitment Strategy | cs.AI | There has been evidence that least-commitment planners can efficiently handle
planning problems that involve difficult goal interactions. This evidence has
led to the common belief that delayed-commitment is the "best" possible
planning strategy. However, we recently found evidence that eager-commitment
planners can ... |
cs/9506102 | Induction of First-Order Decision Lists: Results on Learning the Past
Tense of English Verbs | cs.AI | This paper presents a method for inducing logic programs from examples that
learns a new class of concepts called first-order decision lists, defined as
ordered lists of clauses each ending in a cut. The method, called FOIDL, is
based on FOIL (Quinlan, 1990) but employs intensional background knowledge and
avoids the... |
cs/9507101 | Building and Refining Abstract Planning Cases by Change of
Representation Language | cs.AI | ion is one of the most promising approaches to improve the performance of
problem solvers. In several domains abstraction by dropping sentences of a
domain description -- as used in most hierarchical planners -- has proven
useful. In this paper we present examples which illustrate significant
drawbacks of abstraction... |
cs/9508101 | Using Qualitative Hypotheses to Identify Inaccurate Data | cs.AI | Identifying inaccurate data has long been regarded as a significant and
difficult problem in AI. In this paper, we present a new method for identifying
inaccurate data on the basis of qualitative correlations among related data.
First, we introduce the definitions of related data and qualitative
correlations among re... |
cs/9508102 | An Integrated Framework for Learning and Reasoning | cs.AI | Learning and reasoning are both aspects of what is considered to be
intelligence. Their studies within AI have been separated historically,
learning being the topic of machine learning and neural networks, and reasoning
falling under classical (or symbolic) AI. However, learning and reasoning are
in many ways interde... |
cs/9510101 | Diffusion of Context and Credit Information in Markovian Models | cs.AI | This paper studies the problem of ergodicity of transition probability
matrices in Markovian models, such as hidden Markov models (HMMs), and how it
makes very difficult the task of learning to represent long-term context for
sequential data. This phenomenon hurts the forward propagation of long-term
context informat... |
cs/9510102 | Improving Connectionist Energy Minimization | cs.AI | Symmetric networks designed for energy minimization such as Boltzman machines
and Hopfield nets are frequently investigated for use in optimization,
constraint satisfaction and approximation of NP-hard problems. Nevertheless,
finding a global solution (i.e., a global minimum for the energy function) is
not guaranteed... |
cs/9510103 | Learning Membership Functions in a Function-Based Object Recognition
System | cs.AI | Functionality-based recognition systems recognize objects at the category
level by reasoning about how well the objects support the expected function.
Such systems naturally associate a ``measure of goodness'' or ``membership
value'' with a recognized object. This measure of goodness is the result of
combining indivi... |
cs/9511101 | Flexibly Instructable Agents | cs.AI | This paper presents an approach to learning from situated, interactive
tutorial instruction within an ongoing agent. Tutorial instruction is a
flexible (and thus powerful) paradigm for teaching tasks because it allows an
instructor to communicate whatever types of knowledge an agent might need in
whatever situations ... |
cs/9512101 | OPUS: An Efficient Admissible Algorithm for Unordered Search | cs.AI | OPUS is a branch and bound search algorithm that enables efficient admissible
search through spaces for which the order of search operator application is not
significant. The algorithm's search efficiency is demonstrated with respect to
very large machine learning search spaces. The use of admissible search is of
pot... |
cs/9512102 | Vision-Based Road Detection in Automotive Systems: A Real-Time
Expectation-Driven Approach | cs.AI | The main aim of this work is the development of a vision-based road detection
system fast enough to cope with the difficult real-time constraints imposed by
moving vehicle applications. The hardware platform, a special-purpose massively
parallel system, has been chosen to minimize system production and operational
co... |
cs/9512103 | Generalization of Clauses under Implication | cs.AI | In the area of inductive learning, generalization is a main operation, and
the usual definition of induction is based on logical implication. Recently
there has been a rising interest in clausal representation of knowledge in
machine learning. Almost all inductive learning systems that perform
generalization of claus... |
cs/9512104 | Decision-Theoretic Foundations for Causal Reasoning | cs.AI | We present a definition of cause and effect in terms of decision-theoretic
primitives and thereby provide a principled foundation for causal reasoning.
Our definition departs from the traditional view of causation in that causal
assertions may vary with the set of decisions available. We argue that this
approach prov... |
cs/9512105 | Translating between Horn Representations and their Characteristic Models | cs.AI | Characteristic models are an alternative, model based, representation for
Horn expressions. It has been shown that these two representations are
incomparable and each has its advantages over the other. It is therefore
natural to ask what is the cost of translating, back and forth, between these
representations. Inter... |
cs/9512106 | Statistical Feature Combination for the Evaluation of Game Positions | cs.AI | This article describes an application of three well-known statistical methods
in the field of game-tree search: using a large number of classified Othello
positions, feature weights for evaluation functions with a
game-phase-independent meaning are estimated by means of logistic regression,
Fisher's linear discrimina... |
cs/9512107 | Rule-based Machine Learning Methods for Functional Prediction | cs.AI | We describe a machine learning method for predicting the value of a
real-valued function, given the values of multiple input variables. The method
induces solutions from samples in the form of ordered disjunctive normal form
(DNF) decision rules. A central objective of the method and representation is
the induction o... |
cs/9601101 | The Design and Experimental Analysis of Algorithms for Temporal
Reasoning | cs.AI | Many applications -- from planning and scheduling to problems in molecular
biology -- rely heavily on a temporal reasoning component. In this paper, we
discuss the design and empirical analysis of algorithms for a temporal
reasoning system based on Allen's influential interval-based framework for
representing tempora... |
cs/9602101 | Well-Founded Semantics for Extended Logic Programs with Dynamic
Preferences | cs.AI | The paper describes an extension of well-founded semantics for logic programs
with two types of negation. In this extension information about preferences
between rules can be expressed in the logical language and derived dynamically.
This is achieved by using a reserved predicate symbol and a naming technique.
Confli... |
cs/9602102 | Logarithmic-Time Updates and Queries in Probabilistic Networks | cs.AI | Traditional databases commonly support efficient query and update procedures
that operate in time which is sublinear in the size of the database. Our goal
in this paper is to take a first step toward dynamic reasoning in probabilistic
databases with comparable efficiency. We propose a dynamic data structure that
supp... |
cs/9603101 | Quantum Computing and Phase Transitions in Combinatorial Search | cs.AI | We introduce an algorithm for combinatorial search on quantum computers that
is capable of significantly concentrating amplitude into solutions for some NP
search problems, on average. This is done by exploiting the same aspects of
problem structure as used by classical backtrack methods to avoid unproductive
search ... |
cs/9603102 | Mean Field Theory for Sigmoid Belief Networks | cs.AI | We develop a mean field theory for sigmoid belief networks based on ideas
from statistical mechanics. Our mean field theory provides a tractable
approximation to the true probability distribution in these networks; it also
yields a lower bound on the likelihood of evidence. We demonstrate the utility
of this framewor... |
cs/9603103 | Improved Use of Continuous Attributes in C4.5 | cs.AI | A reported weakness of C4.5 in domains with continuous attributes is
addressed by modifying the formation and evaluation of tests on continuous
attributes. An MDL-inspired penalty is applied to such tests, eliminating some
of them from consideration and altering the relative desirability of all tests.
Empirical trial... |
cs/9603104 | Active Learning with Statistical Models | cs.AI | For many types of machine learning algorithms, one can compute the
statistically `optimal' way to select training data. In this paper, we review
how optimal data selection techniques have been used with feedforward neural
networks. We then show how the same principles may be used to select data for
two alternative, s... |
cs/9604101 | A Divergence Critic for Inductive Proof | cs.AI | Inductive theorem provers often diverge. This paper describes a simple
critic, a computer program which monitors the construction of inductive proofs
attempting to identify diverging proof attempts. Divergence is recognized by
means of a ``difference matching'' procedure. The critic then proposes lemmas
and generaliz... |
cs/9604102 | Practical Methods for Proving Termination of General Logic Programs | cs.AI | Termination of logic programs with negated body atoms (here called general
logic programs) is an important topic. One reason is that many computational
mechanisms used to process negated atoms, like Clark's negation as failure and
Chan's constructive negation, are based on termination conditions. This paper
introduce... |
cs/9604103 | Iterative Optimization and Simplification of Hierarchical Clusterings | cs.AI | Clustering is often used for discovering structure in data. Clustering
systems differ in the objective function used to evaluate clustering quality
and the control strategy used to search the space of clusterings. Ideally, the
search strategy should consistently construct clusterings of high quality, but
be computati... |
cs/9605101 | Further Experimental Evidence against the Utility of Occam's Razor | cs.AI | This paper presents new experimental evidence against the utility of Occam's
razor. A~systematic procedure is presented for post-processing decision trees
produced by C4.5. This procedure was derived by rejecting Occam's razor and
instead attending to the assumption that similar objects are likely to belong
to the sa... |
cs/9605102 | Least Generalizations and Greatest Specializations of Sets of Clauses | cs.AI | The main operations in Inductive Logic Programming (ILP) are generalization
and specialization, which only make sense in a generality order. In ILP, the
three most important generality orders are subsumption, implication and
implication relative to background knowledge. The two languages used most often
are languages... |
cs/9605103 | Reinforcement Learning: A Survey | cs.AI | This paper surveys the field of reinforcement learning from a
computer-science perspective. It is written to be accessible to researchers
familiar with machine learning. Both the historical basis of the field and a
broad selection of current work are summarized. Reinforcement learning is the
problem faced by an agent... |
cs/9605104 | Adaptive Problem-solving for Large-scale Scheduling Problems: A Case
Study | cs.AI | Although most scheduling problems are NP-hard, domain specific techniques
perform well in practice but are quite expensive to construct. In adaptive
problem-solving solving, domain specific knowledge is acquired automatically
for a general problem solver with a flexible control architecture. In this
approach, a learn... |
cs/9605105 | A Formal Framework for Speedup Learning from Problems and Solutions | cs.AI | Speedup learning seeks to improve the computational efficiency of problem
solving with experience. In this paper, we develop a formal framework for
learning efficient problem solving from random problems and their solutions. We
apply this framework to two different representations of learned knowledge,
namely control... |
cs/9605106 | 2Planning for Contingencies: A Decision-based Approach | cs.AI | A fundamental assumption made by classical AI planners is that there is no
uncertainty in the world: the planner has full knowledge of the conditions
under which the plan will be executed and the outcome of every action is fully
predictable. These planners cannot therefore construct contingency plans, i.e.,
plans in ... |
cs/9606101 | A Principled Approach Towards Symbolic Geometric Constraint Satisfaction | cs.AI | An important problem in geometric reasoning is to find the configuration of a
collection of geometric bodies so as to satisfy a set of given constraints.
Recently, it has been suggested that this problem can be solved efficiently by
symbolically reasoning about geometry. This approach, called degrees of freedom
analy... |
cs/9606102 | On Partially Controlled Multi-Agent Systems | cs.AI | Motivated by the control theoretic distinction between controllable and
uncontrollable events, we distinguish between two types of agents within a
multi-agent system: controllable agents, which are directly controlled by the
system's designer, and uncontrollable agents, which are not under the
designer's direct contr... |
cs/9608103 | Spatial Aggregation: Theory and Applications | cs.AI | Visual thinking plays an important role in scientific reasoning. Based on the
research in automating diverse reasoning tasks about dynamical systems,
nonlinear controllers, kinematic mechanisms, and fluid motion, we have
identified a style of visual thinking, imagistic reasoning. Imagistic reasoning
organizes computa... |
cs/9608104 | A Hierarchy of Tractable Subsets for Computing Stable Models | cs.AI | Finding the stable models of a knowledge base is a significant computational
problem in artificial intelligence. This task is at the computational heart of
truth maintenance systems, autoepistemic logic, and default logic.
Unfortunately, it is NP-hard. In this paper we present a hierarchy of classes
of knowledge base... |
cs/9609101 | Accelerating Partial-Order Planners: Some Techniques for Effective
Search Control and Pruning | cs.AI | We propose some domain-independent techniques for bringing well-founded
partial-order planners closer to practicality. The first two techniques are
aimed at improving search control while keeping overhead costs low. One is
based on a simple adjustment to the default A* heuristic used by UCPOP to
select plans for refi... |
cs/9609102 | Cue Phrase Classification Using Machine Learning | cs.AI | Cue phrases may be used in a discourse sense to explicitly signal discourse
structure, but also in a sentential sense to convey semantic rather than
structural information. Correctly classifying cue phrases as discourse or
sentential is critical in natural language processing systems that exploit
discourse structure,... |
cs/9610101 | Mechanisms for Automated Negotiation in State Oriented Domains | cs.AI | This paper lays part of the groundwork for a domain theory of negotiation,
that is, a way of classifying interactions so that it is clear, given a domain,
which negotiation mechanisms and strategies are appropriate. We define State
Oriented Domains, a general category of interaction. Necessary and sufficient
conditio... |
cs/9610102 | Learning First-Order Definitions of Functions | cs.AI | First-order learning involves finding a clause-form definition of a relation
from examples of the relation and relevant background information. In this
paper, a particular first-order learning system is modified to customize it for
finding definitions of functional relations. This restriction leads to faster
learning... |
cs/9611101 | MUSE CSP: An Extension to the Constraint Satisfaction Problem | cs.AI | This paper describes an extension to the constraint satisfaction problem
(CSP) called MUSE CSP (MUltiply SEgmented Constraint Satisfaction Problem).
This extension is especially useful for those problems which segment into
multiple sets of partially shared variables. Such problems arise naturally in
signal processing... |
cs/9612101 | Exploiting Causal Independence in Bayesian Network Inference | cs.AI | A new method is proposed for exploiting causal independencies in exact
Bayesian network inference. A Bayesian network can be viewed as representing a
factorization of a joint probability into the multiplication of a set of
conditional probabilities. We present a notion of causal independence that
enables one to furth... |
cs/9612102 | Quantitative Results Comparing Three Intelligent Interfaces for
Information Capture: A Case Study Adding Name Information into an Electronic
Personal Organizer | cs.AI | Efficiently entering information into a computer is key to enjoying the
benefits of computing. This paper describes three intelligent user interfaces:
handwriting recognition, adaptive menus, and predictive fillin. In the context
of adding a personUs name and address to an electronic organizer, tests show
handwriting... |
cs/9612103 | Characterizations of Decomposable Dependency Models | cs.AI | Decomposable dependency models possess a number of interesting and useful
properties. This paper presents new characterizations of decomposable models in
terms of independence relationships, which are obtained by adding a single
axiom to the well-known set characterizing dependency models that are
isomorphic to undir... |
cs/9701101 | Improved Heterogeneous Distance Functions | cs.AI | Instance-based learning techniques typically handle continuous and linear
input values well, but often do not handle nominal input attributes
appropriately. The Value Difference Metric (VDM) was designed to find
reasonable distance values between nominal attribute values, but it largely
ignores continuous attributes,... |
cs/9701102 | SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis
Using Artificial Neural Networks | cs.AI | Previous approaches of analyzing spontaneously spoken language often have
been based on encoding syntactic and semantic knowledge manually and
symbolically. While there has been some progress using statistical or
connectionist language models, many current spoken- language systems still use
a relatively brittle, hand... |
cs/9703101 | A Uniform Framework for Concept Definitions in Description Logics | cs.AI | Most modern formalisms used in Databases and Artificial Intelligence for
describing an application domain are based on the notions of class (or concept)
and relationship among classes. One interesting feature of such formalisms is
the possibility of defining a class, i.e., providing a set of properties that
precisely... |
cs/9704101 | Lifeworld Analysis | cs.AI | We argue that the analysis of agent/environment interactions should be
extended to include the conventions and invariants maintained by agents
throughout their activity. We refer to this thicker notion of environment as a
lifeworld and present a partial set of formal tools for describing structures
of lifeworlds and ... |
cs/9705101 | Query DAGs: A Practical Paradigm for Implementing Belief-Network
Inference | cs.AI | We describe a new paradigm for implementing inference in belief networks,
which consists of two steps: (1) compiling a belief network into an arithmetic
expression called a Query DAG (Q-DAG); and (2) answering queries using a simple
evaluation algorithm. Each node of a Q-DAG represents a numeric operation, a
number, ... |
cs/9705102 | Connectionist Theory Refinement: Genetically Searching the Space of
Network Topologies | cs.AI | An algorithm that learns from a set of examples should ideally be able to
exploit the available resources of (a) abundant computing power and (b)
domain-specific knowledge to improve its ability to generalize. Connectionist
theory-refinement systems, which use background knowledge to select a neural
network's topolog... |
cs/9706101 | Flaw Selection Strategies for Partial-Order Planning | cs.AI | Several recent studies have compared the relative efficiency of alternative
flaw selection strategies for partial-order causal link (POCL) planning. We
review this literature, and present new experimental results that generalize
the earlier work and explain some of the discrepancies in it. In particular, we
describe ... |
cs/9706102 | A Complete Classification of Tractability in RCC-5 | cs.AI | We investigate the computational properties of the spatial algebra RCC-5
which is a restricted version of the RCC framework for spatial reasoning. The
satisfiability problem for RCC-5 is known to be NP-complete but not much is
known about its approximately four billion subclasses. We provide a complete
classification... |
cs/9707101 | A New Look at the Easy-Hard-Easy Pattern of Combinatorial Search
Difficulty | cs.AI | The easy-hard-easy pattern in the difficulty of combinatorial search problems
as constraints are added has been explained as due to a competition between the
decrease in number of solutions and increased pruning. We test the generality
of this explanation by examining one of its predictions: if the number of
solution... |
cs/9707102 | Eight Maximal Tractable Subclasses of Allen's Algebra with Metric Time | cs.AI | This paper combines two important directions of research in temporal
resoning: that of finding maximal tractable subclasses of Allen's interval
algebra, and that of reasoning with metric temporal information. Eight new
maximal tractable subclasses of Allen's interval algebra are presented, some of
them subsuming prev... |
cs/9707103 | Defining Relative Likelihood in Partially-Ordered Preferential
Structures | cs.AI | Starting with a likelihood or preference order on worlds, we extend it to a
likelihood ordering on sets of worlds in a natural way, and examine the
resulting logic. Lewis earlier considered such a notion of relative likelihood
in the context of studying counterfactuals, but he assumed a total preference
order on worl... |
cs/9709101 | Towards Flexible Teamwork | cs.AI | Many AI researchers are today striving to build agent teams for complex,
dynamic multi-agent domains, with intended applications in arenas such as
education, training, entertainment, information integration, and collective
robotics. Unfortunately, uncertainties in these complex, dynamic domains
obstruct coherent team... |
cs/9709102 | Identifying Hierarchical Structure in Sequences: A linear-time algorithm | cs.AI | SEQUITUR is an algorithm that infers a hierarchical structure from a sequence
of discrete symbols by replacing repeated phrases with a grammatical rule that
generates the phrase, and continuing this process recursively. The result is a
hierarchical representation of the original sequence, which offers insights
into i... |
cs/9710101 | Analysis of Three-Dimensional Protein Images | cs.AI q-bio | A fundamental goal of research in molecular biology is to understand protein
structure. Protein crystallography is currently the most successful method for
determining the three-dimensional (3D) conformation of a protein, yet it
remains labor intensive and relies on an expert's ability to derive and
evaluate a protei... |
cs/9711102 | Storing and Indexing Plan Derivations through Explanation-based Analysis
of Retrieval Failures | cs.AI | Case-Based Planning (CBP) provides a way of scaling up domain-independent
planning to solve large problems in complex domains. It replaces the detailed
and lengthy search for a solution with the retrieval and adaptation of previous
planning experiences. In general, CBP has been demonstrated to improve
performance ove... |
cs/9711103 | A Model Approximation Scheme for Planning in Partially Observable
Stochastic Domains | cs.AI | Partially observable Markov decision processes (POMDPs) are a natural model
for planning problems where effects of actions are nondeterministic and the
state of the world is not completely observable. It is difficult to solve
POMDPs exactly. This paper proposes a new approximation scheme. The basic idea
is to transfo... |
cs/9711104 | Dynamic Non-Bayesian Decision Making | cs.AI | The model of a non-Bayesian agent who faces a repeated game with incomplete
information against Nature is an appropriate tool for modeling general
agent-environment interactions. In such a model the environment state
(controlled by Nature) may change arbitrarily, and the feedback/reward function
is initially unknown.... |
cs/9712101 | When Gravity Fails: Local Search Topology | cs.AI | Local search algorithms for combinatorial search problems frequently
encounter a sequence of states in which it is impossible to improve the value
of the objective function; moves through these regions, called plateau moves,
dominate the time spent in local search. We analyze and characterize plateaus
for three diffe... |
cs/9712102 | Bidirectional Heuristic Search Reconsidered | cs.AI | The assessment of bidirectional heuristic search has been incorrect since it
was first published more than a quarter of a century ago. For quite a long
time, this search strategy did not achieve the expected results, and there was
a major misunderstanding about the reasons behind it. Although there is still
wide-spre... |
cs/9801101 | Incremental Recompilation of Knowledge | cs.AI | Approximating a general formula from above and below by Horn formulas (its
Horn envelope and Horn core, respectively) was proposed by Selman and Kautz
(1991, 1996) as a form of ``knowledge compilation,'' supporting rapid
approximate reasoning; on the negative side, this scheme is static in that it
supports no updates... |
cs/9801102 | Monotonicity and Persistence in Preferential Logics | cs.AI | An important characteristic of many logics for Artificial Intelligence is
their nonmonotonicity. This means that adding a formula to the premises can
invalidate some of the consequences. There may, however, exist formulae that
can always be safely added to the premises without destroying any of the
consequences: we s... |
cs/9803101 | Synthesizing Customized Planners from Specifications | cs.AI | Existing plan synthesis approaches in artificial intelligence fall into two
categories -- domain independent and domain dependent. The domain independent
approaches are applicable across a variety of domains, but may not be very
efficient in any one given domain. The domain dependent approaches need to be
(re)designe... |
cs/9803102 | Cached Sufficient Statistics for Efficient Machine Learning with Large
Datasets | cs.AI | This paper introduces new algorithms and data structures for quick counting
for machine learning datasets. We focus on the counting task of constructing
contingency tables, but our approach is also applicable to counting the number
of records in a dataset that match conjunctive queries. Subject to certain
assumptions... |
cs/9803103 | Tractability of Theory Patching | cs.AI | In this paper we consider the problem of `theory patching', in which we are
given a domain theory, some of whose components are indicated to be possibly
flawed, and a set of labeled training examples for the domain concept. The
theory patching problem is to revise only the indicated components of the
theory, such tha... |
cs/9805101 | Integrative Windowing | cs.AI | In this paper we re-investigate windowing for rule learning algorithms. We
show that, contrary to previous results for decision tree learning, windowing
can in fact achieve significant run-time gains in noise-free domains and
explain the different behavior of rule learning algorithms by the fact that
they learn each ... |
cs/9806101 | Model-Based Diagnosis using Structured System Descriptions | cs.AI | This paper presents a comprehensive approach for model-based diagnosis which
includes proposals for characterizing and computing preferred diagnoses,
assuming that the system description is augmented with a system structure (a
directed graph explicating the interconnections between system components).
Specifically, w... |
cs/9806102 | A Selective Macro-learning Algorithm and its Application to the NxN
Sliding-Tile Puzzle | cs.AI | One of the most common mechanisms used for speeding up problem solvers is
macro-learning. Macros are sequences of basic operators acquired during problem
solving. Macros are used by the problem solver as if they were basic operators.
The major problem that macro-learning presents is the vast number of macros
that are... |
cs/9808001 | Chess Pure Strategies are Probably Chaotic | cs.CC cs.AI | It is odd that chess grandmasters often disagree in their analysis of
positions, sometimes even of simple ones, and that a grandmaster can hold his
own against an powerful analytic machine such as Deep Blue. The fact that there
must exist pure winning strategies for chess is used to construct a control
strategy funct... |
cs/9808005 | First-Order Conditional Logic Revisited | cs.AI cs.LO | Conditional logics play an important role in recent attempts to formulate
theories of default reasoning. This paper investigates first-order conditional
logic. We show that, as for first-order probabilistic logic, it is important
not to confound statistical conditionals over the domain (such as ``most birds
fly''), a... |
cs/9808006 | Set-Theoretic Completeness for Epistemic and Conditional Logic | cs.AI cs.LO | The standard approach to logic in the literature in philosophy and
mathematics, which has also been adopted in computer science, is to define a
language (the syntax), an appropriate class of models together with an
interpretation of formulas in the language (the semantics), a collection of
axioms and rules of inferen... |
cs/9808007 | Plausibility Measures and Default Reasoning | cs.AI cs.LO | We introduce a new approach to modeling uncertainty based on plausibility
measures. This approach is easily seen to generalize other approaches to
modeling uncertainty, such as probability measures, belief functions, and
possibility measures. We focus on one application of plausibility measures in
this paper: default... |
cs/9808101 | The Computational Complexity of Probabilistic Planning | cs.AI | We examine the computational complexity of testing and finding small plans in
probabilistic planning domains with both flat and propositional
representations. The complexity of plan evaluation and existence varies with
the plan type sought; we examine totally ordered plans, acyclic plans, and
looping plans, and parti... |
cs/9809004 | Performance / Price Sort | cs.DB cs.PF | NTsort is an external sort on WindowsNT 5.0. It has minimal functionality but
excellent price performance. In particular, running on mail-order hardware it
can sort 1.5 GB for a penny. For commercially available sorts, Postman Sort
from Robert Ramey Software Development has elapsed time performance comparable
to NTso... |
cs/9809005 | The Five-Minute Rule Ten Years Later, and Other Computer Storage Rules
of Thumb | cs.DB | Simple economic and performance arguments suggest appropriate lifetimes for
main memory pages and suggest optimal page sizes. The fundamental tradeoffs are
the prices and bandwidths of RAMs and disks. The analysis indicates that with
today's technology, five minutes is a good lifetime for randomly accessed
pages, one... |
cs/9809011 | Microsoft TerraServer | cs.DB cs.DL | The Microsoft TerraServer stores aerial and satellite images of the earth in
a SQL Server Database served to the public via the Internet. It is the world's
largest atlas, combining five terabytes of image data from the United States
Geodetic Survey, Sovinformsputnik, and Encarta Virtual Globe. Internet browsers
provi... |
cs/9809013 | Reasoning about Noisy Sensors and Effectors in the Situation Calculus | cs.AI cs.LO | Agents interacting with an incompletely known world need to be able to reason
about the effects of their actions, and to gain further information about that
world they need to use sensors of some sort. Unfortunately, both the effects of
actions and the information returned from sensors are subject to error. To cope
w... |
cs/9809019 | Distributed Computation as Hierarchy | cs.DC cs.NE | This paper presents a new distributed computational model of distributed
systems called the phase web that extends V. Pratt's orthocurrence relation
from 1986. The model uses mutual-exclusion to express sequence, and a new kind
of hierarchy to replace event sequences, posets, and pomsets. The model
explicitly connect... |
cs/9809020 | Linear Segmentation and Segment Significance | cs.CL | We present a new method for discovering a segmental discourse structure of a
document while categorizing segment function. We demonstrate how retrieval of
noun phrases and pronominal forms, along with a zero-sum weighting scheme,
determines topicalized segmentation. Futhermore, we use term distribution to
aid in iden... |
cs/9809021 | Producing NLP-based On-line Contentware | cs.CL cs.AR | For its internal needs as well as for commercial purposes, CDC Group has
produced several NLP-based on-line contentware applications for years. The
development process of such applications is subject to numerous constraints
such as quality of service, integration of new advances in NLP, direct
reactions from users, c... |
cs/9809022 | Modelling Users, Intentions, and Structure in Spoken Dialog | cs.CL | We outline how utterances in dialogs can be interpreted using a partial first
order logic. We exploit the capability of this logic to talk about the truth
status of formulae to define a notion of coherence between utterances and
explain how this coherence relation can serve for the construction of AND/OR
trees that r... |
cs/9809023 | Similarity-Based Queries for Time Series Data | cs.DB | We study a set of linear transformations on the Fourier series representation
of a sequence that can be used as the basis for similarity queries on
time-series data. We show that our set of transformations is rich enough to
formulate operations such as moving average and time warping. We present a
query processing al... |
cs/9809024 | A Lexicalized Tree Adjoining Grammar for English | cs.CL | This document describes a sizable grammar of English written in the TAG
formalism and implemented for use with the XTAG system. This report and the
grammar described herein supersedes the TAG grammar described in an earlier
1995 XTAG technical report. The English grammar described in this report is
based on the TAG f... |
cs/9809025 | Novelty and Social Search in the World Wide Web | cs.MA cs.DL | The World Wide Web is fast becoming a source of information for a large part
of the world's population. Because of its sheer size and complexity users often
resort to recommendations from others to decide which sites to visit. We
present a dynamical theory of recommendations which predicts site visits by
users of the... |
cs/9809026 | Prefix Probabilities from Stochastic Tree Adjoining Grammars | cs.CL | Language models for speech recognition typically use a probability model of
the form Pr(a_n | a_1, a_2, ..., a_{n-1}). Stochastic grammars, on the other
hand, are typically used to assign structure to utterances. A language model of
the above form is constructed from such grammars by computing the prefix
probability ... |
cs/9809027 | Conditions on Consistency of Probabilistic Tree Adjoining Grammars | cs.CL | Much of the power of probabilistic methods in modelling language comes from
their ability to compare several derivations for the same string in the
language. An important starting point for the study of such cross-derivational
properties is the notion of _consistency_. The probability model defined by a
probabilistic... |
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