title string | abstract string |
|---|---|
Dynamic Backtracking | Because of their occasional need to return to shallow points in a search
tree, existing backtracking methods can sometimes erase meaningful progress
toward solving a search problem. In this paper, we present a method by which
backtrack points can be moved deeper in the search space, thereby avoiding this
difficulty. Th... |
A Market-Oriented Programming Environment and its Application to
Distributed Multicommodity Flow Problems | Market price systems constitute a well-understood class of mechanisms that
under certain conditions provide effective decentralization of decision making
with minimal communication overhead. In a market-oriented programming approach
to distributed problem solving, we derive the activities and resource
allocations for a... |
An Empirical Analysis of Search in GSAT | We describe an extensive study of search in GSAT, an approximation procedure
for propositional satisfiability. GSAT performs greedy hill-climbing on the
number of satisfied clauses in a truth assignment. Our experiments provide a
more complete picture of GSAT's search than previous accounts. We describe in
detail the t... |
The Difficulties of Learning Logic Programs with Cut | As real logic programmers normally use cut (!), an effective learning
procedure for logic programs should be able to deal with it. Because the cut
predicate has only a procedural meaning, clauses containing cut cannot be
learned using an extensional evaluation method, as is done in most learning
systems. On the other h... |
Software Agents: Completing Patterns and Constructing User Interfaces | To support the goal of allowing users to record and retrieve information,
this paper describes an interactive note-taking system for pen-based computers
with two distinctive features. First, it actively predicts what the user is
going to write. Second, it automatically constructs a custom, button-box user
interface on ... |
Decidable Reasoning in Terminological Knowledge Representation Systems | Terminological knowledge representation systems (TKRSs) are tools for
designing and using knowledge bases that make use of terminological languages
(or concept languages). We analyze from a theoretical point of view a TKRS
whose capabilities go beyond the ones of presently available TKRSs. The new
features studied, oft... |
Teleo-Reactive Programs for Agent Control | A formalism is presented for computing and organizing actions for autonomous
agents in dynamic environments. We introduce the notion of teleo-reactive (T-R)
programs whose execution entails the construction of circuitry for the
continuous computation of the parameters and conditions on which agent action
is based. In a... |
Learning the Past Tense of English Verbs: The Symbolic Pattern
Associator vs. Connectionist Models | Learning the past tense of English verbs - a seemingly minor aspect of
language acquisition - has generated heated debates since 1986, and has become
a landmark task for testing the adequacy of cognitive modeling. Several
artificial neural networks (ANNs) have been implemented, and a challenge for
better symbolic model... |
Substructure Discovery Using Minimum Description Length and Background
Knowledge | The ability to identify interesting and repetitive substructures is an
essential component to discovering knowledge in structural data. We describe a
new version of our SUBDUE substructure discovery system based on the minimum
description length principle. The SUBDUE system discovers substructures that
compress the ori... |
Bias-Driven Revision of Logical Domain Theories | The theory revision problem is the problem of how best to go about revising a
deficient domain theory using information contained in examples that expose
inaccuracies. In this paper we present our approach to the theory revision
problem for propositional domain theories. The approach described here, called
PTR, uses pr... |
Exploring the Decision Forest: An Empirical Investigation of Occam's
Razor in Decision Tree Induction | We report on a series of experiments in which all decision trees consistent
with the training data are constructed. These experiments were run to gain an
understanding of the properties of the set of consistent decision trees and the
factors that affect the accuracy of individual trees. In particular, we
investigated t... |
A Semantics and Complete Algorithm for Subsumption in the CLASSIC
Description Logic | This paper analyzes the correctness of the subsumption algorithm used in
CLASSIC, a description logic-based knowledge representation system that is
being used in practical applications. In order to deal efficiently with
individuals in CLASSIC descriptions, the developers have had to use an
algorithm that is incomplete ... |
Applying GSAT to Non-Clausal Formulas | In this paper we describe how to modify GSAT so that it can be applied to
non-clausal formulas. The idea is to use a particular ``score'' function which
gives the number of clauses of the CNF conversion of a formula which are false
under a given truth assignment. Its value is computed in linear time, without
constructi... |
Random Worlds and Maximum Entropy | Given a knowledge base KB containing first-order and statistical facts, we
consider a principled method, called the random-worlds method, for computing a
degree of belief that some formula Phi holds given KB. If we are reasoning
about a world or system consisting of N individuals, then we can consider all
possible worl... |
Pattern Matching and Discourse Processing in Information Extraction from
Japanese Text | Information extraction is the task of automatically picking up information of
interest from an unconstrained text. Information of interest is usually
extracted in two steps. First, sentence level processing locates relevant
pieces of information scattered throughout the text; second, discourse
processing merges corefer... |
A System for Induction of Oblique Decision Trees | This article describes a new system for induction of oblique decision trees.
This system, OC1, combines deterministic hill-climbing with two forms of
randomization to find a good oblique split (in the form of a hyperplane) at
each node of a decision tree. Oblique decision tree methods are tuned
especially for domains i... |
On Planning while Learning | This paper introduces a framework for Planning while Learning where an agent
is given a goal to achieve in an environment whose behavior is only partially
known to the agent. We discuss the tractability of various plan-design
processes. We show that for a large natural class of Planning while Learning
systems, a plan c... |
Wrap-Up: a Trainable Discourse Module for Information Extraction | The vast amounts of on-line text now available have led to renewed interest
in information extraction (IE) systems that analyze unrestricted text,
producing a structured representation of selected information from the text.
This paper presents a novel approach that uses machine learning to acquire
knowledge for some of... |
Operations for Learning with Graphical Models | This paper is a multidisciplinary review of empirical, statistical learning
from a graphical model perspective. Well-known examples of graphical models
include Bayesian networks, directed graphs representing a Markov chain, and
undirected networks representing a Markov field. These graphical models are
extended to mode... |
Total-Order and Partial-Order Planning: A Comparative Analysis | For many years, the intuitions underlying partial-order planning were largely
taken for granted. Only in the past few years has there been renewed interest
in the fundamental principles underlying this paradigm. In this paper, we
present a rigorous comparative analysis of partial-order and total-order
planning by focus... |
Solving Multiclass Learning Problems via Error-Correcting Output Codes | 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 in... |
A Domain-Independent Algorithm for Plan Adaptation | 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 sys... |
Truncating Temporal Differences: On the Efficient Implementation of
TD(lambda) for Reinforcement Learning | 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 alg... |
Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic
Decision Tree Induction Algorithm | 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 cost... |
Rerepresenting and Restructuring Domain Theories: A Constructive
Induction Approach | 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 may... |
Using Pivot Consistency to Decompose and Solve Functional CSPs | 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 given ... |
Adaptive Load Balancing: A Study in Multi-Agent Learning | 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 nature ... |
Provably Bounded-Optimal Agents | 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 has ... |
Pac-Learning Recursive Logic Programs: Efficient Algorithms | 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-d... |
Pac-learning Recursive Logic Programs: Negative Results | 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 follow... |
FLECS: Planning with a Flexible Commitment Strategy | 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 ha... |
Induction of First-Order Decision Lists: Results on Learning the Past
Tense of English Verbs | 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 n... |
Building and Refining Abstract Planning Cases by Change of
Representation Language | 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 b... |
Using Qualitative Hypotheses to Identify Inaccurate Data | 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 rela... |
An Integrated Framework for Learning and Reasoning | 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 interdepe... |
Diffusion of Context and Credit Information in Markovian Models | 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 informatio... |
Improving Connectionist Energy Minimization | 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 a... |
Learning Membership Functions in a Function-Based Object Recognition
System | 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 individu... |
Flexibly Instructable Agents | 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 mi... |
OPUS: An Efficient Admissible Algorithm for Unordered Search | 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
poten... |
Vision-Based Road Detection in Automotive Systems: A Real-Time
Expectation-Driven Approach | 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
cost... |
Generalization of Clauses under Implication | 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 clauses... |
Decision-Theoretic Foundations for Causal Reasoning | 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 provid... |
Translating between Horn Representations and their Characteristic Models | 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. Interes... |
Statistical Feature Combination for the Evaluation of Game Positions | 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 discriminant... |
Rule-based Machine Learning Methods for Functional Prediction | 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 of ... |
The Design and Experimental Analysis of Algorithms for Temporal
Reasoning | 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 temporal ... |
Well-Founded Semantics for Extended Logic Programs with Dynamic
Preferences | 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.
Conflict... |
Logarithmic-Time Updates and Queries in Probabilistic Networks | 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
suppor... |
Quantum Computing and Phase Transitions in Combinatorial Search | 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 ch... |
Mean Field Theory for Sigmoid Belief Networks | 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 framework ... |
Improved Use of Continuous Attributes in C4.5 | 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 trials ... |
Active Learning with Statistical Models | 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, sta... |
A Divergence Critic for Inductive Proof | 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 generalizat... |
Practical Methods for Proving Termination of General Logic Programs | 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
introduces ... |
Iterative Optimization and Simplification of Hierarchical Clusterings | 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 computation... |
Further Experimental Evidence against the Utility of Occam's Razor | 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 same... |
Least Generalizations and Greatest Specializations of Sets of Clauses | 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 o... |
Reinforcement Learning: A Survey | 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 t... |
Adaptive Problem-solving for Large-scale Scheduling Problems: A Case
Study | 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 learnin... |
A Formal Framework for Speedup Learning from Problems and Solutions | 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 r... |
2Planning for Contingencies: A Decision-based Approach | 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 wh... |
A Principled Approach Towards Symbolic Geometric Constraint Satisfaction | 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
analysi... |
On Partially Controlled Multi-Agent Systems | 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 control... |
Spatial Aggregation: Theory and Applications | 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 computati... |
A Hierarchy of Tractable Subsets for Computing Stable Models | 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 bases,... |
Accelerating Partial-Order Planners: Some Techniques for Effective
Search Control and Pruning | 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 refine... |
Cue Phrase Classification Using Machine Learning | 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, e... |
Mechanisms for Automated Negotiation in State Oriented Domains | 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
conditions... |
Learning First-Order Definitions of Functions | 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 t... |
MUSE CSP: An Extension to the Constraint Satisfaction Problem | 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 a... |
Exploiting Causal Independence in Bayesian Network Inference | 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 further... |
Quantitative Results Comparing Three Intelligent Interfaces for
Information Capture: A Case Study Adding Name Information into an Electronic
Personal Organizer | 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 r... |
Characterizations of Decomposable Dependency Models | 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 undirec... |
Improved Heterogeneous Distance Functions | 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, r... |
SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis
Using Artificial Neural Networks | 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-c... |
A Uniform Framework for Concept Definitions in Description Logics | 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 c... |
Lifeworld Analysis | 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 th... |
Query DAGs: A Practical Paradigm for Implementing Belief-Network
Inference | 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, or... |
Connectionist Theory Refinement: Genetically Searching the Space of
Network Topologies | 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 topology ... |
Flaw Selection Strategies for Partial-Order Planning | 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 th... |
A Complete Classification of Tractability in RCC-5 | 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 o... |
A New Look at the Easy-Hard-Easy Pattern of Combinatorial Search
Difficulty | 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
solutions ... |
Eight Maximal Tractable Subclasses of Allen's Algebra with Metric Time | 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 previo... |
Defining Relative Likelihood in Partially-Ordered Preferential
Structures | 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 worlds... |
Towards Flexible Teamwork | 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 teamwo... |
Identifying Hierarchical Structure in Sequences: A linear-time algorithm | 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 its... |
Storing and Indexing Plan Derivations through Explanation-based Analysis
of Retrieval Failures | 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 over ... |
A Model Approximation Scheme for Planning in Partially Observable
Stochastic Domains | 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 transform... |
Dynamic Non-Bayesian Decision Making | 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. T... |
When Gravity Fails: Local Search Topology | 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 differe... |
Bidirectional Heuristic Search Reconsidered | 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-spread... |
Incremental Recompilation of Knowledge | 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, ... |
Monotonicity and Persistence in Preferential Logics | 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 say... |
Synthesizing Customized Planners from Specifications | 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)designed ... |
Cached Sufficient Statistics for Efficient Machine Learning with Large
Datasets | 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, ... |
Tractability of Theory Patching | 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 that ... |
Integrative Windowing | 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 ru... |
Model-Based Diagnosis using Structured System Descriptions | 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, we ... |
A Selective Macro-learning Algorithm and its Application to the NxN
Sliding-Tile Puzzle | 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 a... |
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Comprehensive Academic Papers Dataset: 3M+ Research Paper Titles and Abstracts
📋 Overview
This dataset is a comprehensive collection of over 3 million research paper titles and abstracts, curated and consolidated from multiple high-quality academic sources. The dataset provides a unified, clean, and standardized format for researchers, data scientists, and machine learning practitioners working on natural language processing, academic research analysis, and knowledge discovery tasks.
🎯 Key Features
- 3.6+ million scientific papers with titles and abstracts
- Multi-domain coverage: Physics, Mathematics, Computer Science, Biology, Medicine, and more
- Standardized format: Consistent
titleandabstractcolumns - Quality assured: Validated using Pydantic models and cleaned of duplicates/null values
- Ready-to-use: Pre-processed and formatted for immediate analysis
- Format: CSV
- Language: English
📊 Dataset Statistics
| Metric | Value |
|---|---|
| Total Records | ~3,000,000+ |
| Columns | 2 (title, abstract) |
| File Size | 4.15 GB |
| Format | CSV |
| Duplicates | Removed |
| Missing Values | Removed |
🗂️ Dataset Structure
cleaned_papers.csv
├── title (string): Scientific paper title
└── abstract (string): Scientific paper abstract
🔄 Data Processing Pipeline
The dataset underwent a rigorous cleaning and standardization process:
- Data Import: Automated import from multiple sources (Kaggle API, Hugging Face)
- Column Standardization: Mapping various column names to consistent
titleandabstractformat - Data Validation: Pydantic model validation ensuring data quality
- Duplicate Removal: Advanced deduplication based on title and abstract similarity
- Null Value Handling: Removal of records with missing titles or abstracts
- Quality Assurance: Final validation and statistics generation
💡 Use Cases
This dataset is ideal for:
- Natural Language Processing: Text classification, sentiment analysis, topic modeling
- Scientific Literature Analysis: Trend analysis, domain classification, citation prediction
- Machine Learning Research: Training language models, text summarization, information extraction
- Academic Research: Bibliometric analysis, research trend identification
- Educational Applications: Building search engines, recommendation systems
🔗 Data Sources and Attribution
This dataset consolidates academic papers from the following sources:
Kaggle Datasets:
- ArXiv Scientific Research Papers Dataset by @sumitm004
- Cornell University ArXiv Dataset by @Cornell-University
Hugging Face Datasets:
- ML-ArXiv-Papers by @CShorten
- ArXiv Biology by @zeroshot
- ArXiv Data Extended by @wrapper228
- Stroke PubMed Abstracts by @Gaborandi
- PubMed ArXiv Abstracts Data by @brainchalov
- Abstracts Cleaned by @Eitanli
🔄 Update Schedule
This dataset represents a point-in-time consolidation. Future versions may include:
- Additional academic sources
- Extended fields (authors, publication dates, venues)
- Domain-specific subsets
- Enhanced metadata
📄 License and Usage
Please respect the individual licenses of the source datasets. This consolidated version is provided for research and educational purposes. When using this dataset:
- Citation: Please cite this dataset and acknowledge the original data sources
- Attribution: Credit the original dataset creators listed above
- Compliance: Ensure compliance with individual dataset licenses
- Academic Use: Primarily intended for non-commercial, academic, and research purposes
🙏 Acknowledgments
Special thanks to all the original dataset creators and the academic communities that make their research data publicly available. This work builds upon their valuable contributions to open science and knowledge sharing.
Keywords: academic papers, research abstracts, NLP, machine learning, text mining, scientific literature, ArXiv, PubMed, natural language processing, research dataset
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