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19,702 | Engineering a Conformant Probabilistic Planner | cs.AI | We present a partial-order, conformant, probabilistic planner, Probapop which
competed in the blind track of the Probabilistic Planning Competition in IPC-4.
We explain how we adapt distance based heuristics for use with probabilistic
domains. Probapop also incorporates heuristics based on probability of success.
We ex... | computer science |
19,703 | Where 'Ignoring Delete Lists' Works: Local Search Topology in Planning
Benchmarks | cs.AI | Between 1998 and 2004, the planning community has seen vast progress in terms
of the sizes of benchmark examples that domain-independent planners can tackle
successfully. The key technique behind this progress is the use of heuristic
functions based on relaxing the planning task at hand, where the relaxation is
to assu... | computer science |
19,704 | Binary Encodings of Non-binary Constraint Satisfaction Problems:
Algorithms and Experimental Results | cs.AI | A non-binary Constraint Satisfaction Problem (CSP) can be solved directly
using extended versions of binary techniques. Alternatively, the non-binary
problem can be translated into an equivalent binary one. In this case, it is
generally accepted that the translated problem can be solved by applying
well-established tec... | computer science |
19,705 | Distributed Reasoning in a Peer-to-Peer Setting: Application to the
Semantic Web | cs.AI | In a peer-to-peer inference system, each peer can reason locally but can also
solicit some of its acquaintances, which are peers sharing part of its
vocabulary. In this paper, we consider peer-to-peer inference systems in which
the local theory of each peer is a set of propositional clauses defined upon a
local vocabul... | computer science |
19,706 | Dynamic Local Search for the Maximum Clique Problem | cs.AI | In this paper, we introduce DLS-MC, a new stochastic local search algorithm
for the maximum clique problem. DLS-MC alternates between phases of iterative
improvement, during which suitable vertices are added to the current clique,
and plateau search, during which vertices of the current clique are swapped
with vertices... | computer science |
19,707 | Representing Conversations for Scalable Overhearing | cs.AI | Open distributed multi-agent systems are gaining interest in the academic
community and in industry. In such open settings, agents are often coordinated
using standardized agent conversation protocols. The representation of such
protocols (for analysis, validation, monitoring, etc) is an important aspect of
multi-agent... | computer science |
19,708 | Improving Heuristics Through Relaxed Search - An Analysis of TP4 and
HSP*a in the 2004 Planning Competition | cs.AI | The hm admissible heuristics for (sequential and temporal) regression
planning are defined by a parameterized relaxation of the optimal cost function
in the regression search space, where the parameter m offers a trade-off
between the accuracy and computational cost of theheuristic. Existing methods
for computing the h... | computer science |
19,709 | Models and Strategies for Variants of the Job Shop Scheduling Problem | cs.AI | Recently, a variety of constraint programming and Boolean satisfiability
approaches to scheduling problems have been introduced. They have in common the
use of relatively simple propagation mechanisms and an adaptive way to focus on
the most constrained part of the problem. In some cases, these methods compare
favorabl... | computer science |
19,710 | An Approximation of the Universal Intelligence Measure | cs.AI | The Universal Intelligence Measure is a recently proposed formal definition
of intelligence. It is mathematically specified, extremely general, and
captures the essence of many informal definitions of intelligence. It is based
on Hutter's Universal Artificial Intelligence theory, an extension of Ray
Solomonoff's pionee... | computer science |
19,711 | An Improved Search Algorithm for Optimal Multiple-Sequence Alignment | cs.AI | Multiple sequence alignment (MSA) is a ubiquitous problem in computational
biology. Although it is NP-hard to find an optimal solution for an arbitrary
number of sequences, due to the importance of this problem researchers are
trying to push the limits of exact algorithms further. Since MSA can be cast as
a classical p... | computer science |
19,712 | Probabilistic Hybrid Action Models for Predicting Concurrent
Percept-driven Robot Behavior | cs.AI | This article develops Probabilistic Hybrid Action Models (PHAMs), a realistic
causal model for predicting the behavior generated by modern percept-driven
robot plans. PHAMs represent aspects of robot behavior that cannot be
represented by most action models used in AI planning: the temporal structure
of continuous cont... | computer science |
19,713 | Generative Prior Knowledge for Discriminative Classification | cs.AI | We present a novel framework for integrating prior knowledge into
discriminative classifiers. Our framework allows discriminative classifiers
such as Support Vector Machines (SVMs) to utilize prior knowledge specified in
the generative setting. The dual objective of fitting the data and respecting
prior knowledge is fo... | computer science |
19,714 | The Fast Downward Planning System | cs.AI | Fast Downward is a classical planning system based on heuristic search. It
can deal with general deterministic planning problems encoded in the
propositional fragment of PDDL2.2, including advanced features like ADL
conditions and effects and derived predicates (axioms). Like other well-known
planners such as HSP and F... | computer science |
19,715 | Asynchronous Partial Overlay: A New Algorithm for Solving Distributed
Constraint Satisfaction Problems | cs.AI | Distributed Constraint Satisfaction (DCSP) has long been considered an
important problem in multi-agent systems research. This is because many
real-world problems can be represented as constraint satisfaction and these
problems often present themselves in a distributed form. In this article, we
present a new complete, ... | computer science |
19,716 | Admissible and Restrained Revision | cs.AI | As partial justification of their framework for iterated belief revision
Darwiche and Pearl convincingly argued against Boutiliers natural revision and
provided a prototypical revision operator that fits into their scheme. We show
that the Darwiche-Pearl arguments lead naturally to the acceptance of a smaller
class of ... | computer science |
19,717 | On Graphical Modeling of Preference and Importance | cs.AI | In recent years, CP-nets have emerged as a useful tool for supporting
preference elicitation, reasoning, and representation. CP-nets capture and
support reasoning with qualitative conditional preference statements,
statements that are relatively natural for users to express. In this paper, we
extend the CP-nets formali... | computer science |
19,718 | The Planning Spectrum - One, Two, Three, Infinity | cs.AI | Linear Temporal Logic (LTL) is widely used for defining conditions on the
execution paths of dynamic systems. In the case of dynamic systems that allow
for nondeterministic evolutions, one has to specify, along with an LTL formula
f, which are the paths that are required to satisfy the formula. Two extreme
cases are th... | computer science |
19,719 | Fault Tolerant Boolean Satisfiability | cs.AI | A delta-model is a satisfying assignment of a Boolean formula for which any
small alteration, such as a single bit flip, can be repaired by flips to some
small number of other bits, yielding a new satisfying assignment. These
satisfying assignments represent robust solutions to optimization problems
(e.g., scheduling) ... | computer science |
19,720 | Cognitive Principles in Robust Multimodal Interpretation | cs.AI | Multimodal conversational interfaces provide a natural means for users to
communicate with computer systems through multiple modalities such as speech
and gesture. To build effective multimodal interfaces, automated interpretation
of user multimodal inputs is important. Inspired by the previous investigation
on cogniti... | computer science |
19,721 | Multiple-Goal Heuristic Search | cs.AI | This paper presents a new framework for anytime heuristic search where the
task is to achieve as many goals as possible within the allocated resources. We
show the inadequacy of traditional distance-estimation heuristics for tasks of
this type and present alternative heuristics that are more appropriate for
multiple-go... | computer science |
19,722 | FluCaP: A Heuristic Search Planner for First-Order MDPs | cs.AI | We present a heuristic search algorithm for solving first-order Markov
Decision Processes (FOMDPs). Our approach combines first-order state
abstraction that avoids evaluating states individually, and heuristic search
that avoids evaluating all states. Firstly, in contrast to existing systems,
which start with propositi... | computer science |
19,723 | Learning Dependency-Based Compositional Semantics | cs.AI | Suppose we want to build a system that answers a natural language question by
representing its semantics as a logical form and computing the answer given a
structured database of facts. The core part of such a system is the semantic
parser that maps questions to logical forms. Semantic parsers are typically
trained fro... | computer science |
19,724 | Publishing and linking transport data on the Web | cs.AI | Without Linked Data, transport data is limited to applications exclusively
around transport. In this paper, we present a workflow for publishing and
linking transport data on the Web. So we will be able to develop transport
applications and to add other features which will be created from other
datasets. This will be p... | computer science |
19,725 | An improved approach to attribute reduction with covering rough sets | cs.AI | Attribute reduction is viewed as an important preprocessing step for pattern
recognition and data mining. Most of researches are focused on attribute
reduction by using rough sets. Recently, Tsang et al. discussed attribute
reduction with covering rough sets in the paper [E. C.C. Tsang, D. Chen, Daniel
S. Yeung, Approx... | computer science |
19,726 | Proceedings of the Twenty-Seventh Conference on Uncertainty in
Artificial Intelligence (2011) | cs.AI | This is the Proceedings of the Twenty-Seventh Conference on Uncertainty in
Artificial Intelligence, which was held in Barcelona, Spain, July 14 - 17 2011. | computer science |
19,727 | Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial
Intelligence (2010) | cs.AI | This is the Proceedings of the Twenty-Sixth Conference on Uncertainty in
Artificial Intelligence, which was held on Catalina Island, CA, July 8 - 11
2010. | computer science |
19,728 | Most Relevant Explanation: Properties, Algorithms, and Evaluations | cs.AI | Most Relevant Explanation (MRE) is a method for finding multivariate
explanations for given evidence in Bayesian networks [12]. This paper studies
the theoretical properties of MRE and develops an algorithm for finding
multiple top MRE solutions. Our study shows that MRE relies on an implicit soft
relevance measure in ... | computer science |
19,729 | Measuring Inconsistency in Probabilistic Knowledge Bases | cs.AI | This paper develops an inconsistency measure on conditional probabilistic
knowledge bases. The measure is based on fundamental principles for
inconsistency measures and thus provides a solid theoretical framework for the
treatment of inconsistencies in probabilistic expert systems. We illustrate its
usefulness and imme... | computer science |
19,730 | Bisimulation-based Approximate Lifted Inference | cs.AI | There has been a great deal of recent interest in methods for performing
lifted inference; however, most of this work assumes that the first-order model
is given as input to the system. Here, we describe lifted inference algorithms
that determine symmetries and automatically lift the probabilistic model to
speedup infe... | computer science |
19,731 | Regret-based Reward Elicitation for Markov Decision Processes | cs.AI | The specification of aMarkov decision process (MDP) can be difficult. Reward
function specification is especially problematic; in practice, it is often
cognitively complex and time-consuming for users to precisely specify rewards.
This work casts the problem of specifying rewards as one of preference
elicitation and ai... | computer science |
19,732 | Logical Inference Algorithms and Matrix Representations for
Probabilistic Conditional Independence | cs.AI | Logical inference algorithms for conditional independence (CI) statements
have important applications from testing consistency during knowledge
elicitation to constraintbased structure learning of graphical models. We prove
that the implication problem for CI statements is decidable, given that the
size of the domains ... | computer science |
19,733 | The Temporal Logic of Causal Structures | cs.AI | Computational analysis of time-course data with an underlying causal
structure is needed in a variety of domains, including neural spike trains,
stock price movements, and gene expression levels. However, it can be
challenging to determine from just the numerical time course data alone what is
coordinating the visible ... | computer science |
19,734 | Constraint Processing in Lifted Probabilistic Inference | cs.AI | First-order probabilistic models combine representational power of
first-order logic with graphical models. There is an ongoing effort to design
lifted inference algorithms for first-order probabilistic models. We analyze
lifted inference from the perspective of constraint processing and, through
this viewpoint, we ana... | computer science |
19,735 | Counting Belief Propagation | cs.AI | A major benefit of graphical models is that most knowledge is captured in the
model structure. Many models, however, produce inference problems with a lot of
symmetries not reflected in the graphical structure and hence not exploitable
by efficient inference techniques such as belief propagation (BP). In this
paper, we... | computer science |
19,736 | Improved Mean and Variance Approximations for Belief Net Responses via
Network Doubling | cs.AI | A Bayesian belief network models a joint distribution with an directed
acyclic graph representing dependencies among variables and network parameters
characterizing conditional distributions. The parameters are viewed as random
variables to quantify uncertainty about their values. Belief nets are used to
compute respon... | computer science |
19,737 | Generating Optimal Plans in Highly-Dynamic Domains | cs.AI | Generating optimal plans in highly dynamic environments is challenging. Plans
are predicated on an assumed initial state, but this state can change
unexpectedly during plan generation, potentially invalidating the planning
effort. In this paper we make three contributions: (1) We propose a novel
algorithm for generatin... | computer science |
19,738 | Seeing the Forest Despite the Trees: Large Scale Spatial-Temporal
Decision Making | cs.AI | We introduce a challenging real-world planning problem where actions must be
taken at each location in a spatial area at each point in time. We use forestry
planning as the motivating application. In Large Scale Spatial-Temporal (LSST)
planning problems, the state and action spaces are defined as the
cross-products of ... | computer science |
19,739 | Complexity Analysis and Variational Inference for Interpretation-based
Probabilistic Description Logic | cs.AI | This paper presents complexity analysis and variational methods for inference
in probabilistic description logics featuring Boolean operators,
quantification, qualified number restrictions, nominals, inverse roles and role
hierarchies. Inference is shown to be PEXP-complete, and variational methods
are designed so as t... | computer science |
19,740 | Mean Field Variational Approximation for Continuous-Time Bayesian
Networks | cs.AI | Continuous-time Bayesian networks is a natural structured representation
language for multicomponent stochastic processes that evolve continuously over
time. Despite the compact representation, inference in such models is
intractable even in relatively simple structured networks. Here we introduce a
mean field variatio... | computer science |
19,741 | Deterministic POMDPs Revisited | cs.AI | We study a subclass of POMDPs, called Deterministic POMDPs, that is
characterized by deterministic actions and observations. These models do not
provide the same generality of POMDPs yet they capture a number of interesting
and challenging problems, and permit more efficient algorithms. Indeed, some of
the recent work ... | computer science |
19,742 | Lower Bound Bayesian Networks - An Efficient Inference of Lower Bounds
on Probability Distributions in Bayesian Networks | cs.AI | We present a new method to propagate lower bounds on conditional probability
distributions in conventional Bayesian networks. Our method guarantees to
provide outer approximations of the exact lower bounds. A key advantage is that
we can use any available algorithms and tools for Bayesian networks in order to
represent... | computer science |
19,743 | Operations on soft sets revisited | cs.AI | Soft sets, as a mathematical tool for dealing with uncertainty, have recently
gained considerable attention, including some successful applications in
information processing, decision, demand analysis, and forecasting. To
construct new soft sets from given soft sets, some operations on soft sets have
been proposed. Unf... | computer science |
19,744 | Approximate Modified Policy Iteration | cs.AI | Modified policy iteration (MPI) is a dynamic programming (DP) algorithm that
contains the two celebrated policy and value iteration methods. Despite its
generality, MPI has not been thoroughly studied, especially its approximation
form which is used when the state and/or action spaces are large or infinite.
In this pap... | computer science |
19,745 | Unfair items detection in educational measurement | cs.AI | Measurement professionals cannot come to an agreement on the definition of
the term 'item fairness'. In this paper a continuous measure of item unfairness
is proposed. The more the unfairness measure deviates from zero, the less fair
the item is. If the measure exceeds the cutoff value, the item is identified as
defini... | computer science |
19,746 | Machine Recognition of Hand Written Characters using Neural Networks | cs.AI | Even today in Twenty First Century Handwritten communication has its own
stand and most of the times, in daily life it is globally using as means of
communication and recording the information like to be shared with others.
Challenges in handwritten characters recognition wholly lie in the variation
and distortion of h... | computer science |
19,747 | A Simplified Description of Fuzzy TOPSIS | cs.AI | A simplified description of Fuzzy TOPSIS (Technique for Order Preference by
Similarity to Ideal Situation) is presented. We have adapted the TOPSIS
description from existing Fuzzy theory literature and distilled the bare
minimum concepts required for understanding and applying TOPSIS. An example has
been worked out to ... | computer science |
19,748 | Approximate Equalities on Rough Intuitionistic Fuzzy Sets and an
Analysis of Approximate Equalities | cs.AI | In order to involve user knowledge in determining equality of sets, which may
not be equal in the mathematical sense, three types of approximate (rough)
equalities were introduced by Novotny and Pawlak ([8, 9, 10]). These notions
were generalized by Tripathy, Mitra and Ojha ([13]), who introduced the
concepts of approx... | computer science |
19,749 | Rule Based Expert System for Cerebral Palsy Diagnosis | cs.AI | The use of Artificial Intelligence is finding prominence not only in core
computer areas, but also in cross disciplinary areas including medical
diagnosis. In this paper, we present a rule based Expert System used in
diagnosis of Cerebral Palsy. The expert system takes user input and depending
on the symptoms of the pa... | computer science |
19,750 | Alternative Restart Strategies for CMA-ES | cs.AI | This paper focuses on the restart strategy of CMA-ES on multi-modal
functions. A first alternative strategy proceeds by decreasing the initial
step-size of the mutation while doubling the population size at each restart. A
second strategy adaptively allocates the computational budget among the restart
settings in the B... | computer science |
19,751 | Characteristic matrix of covering and its application to boolean matrix
decomposition and axiomatization | cs.AI | Covering is an important type of data structure while covering-based rough
sets provide an efficient and systematic theory to deal with covering data. In
this paper, we use boolean matrices to represent and axiomatize three types of
covering approximation operators. First, we define two types of characteristic
matrices... | computer science |
19,752 | Robust Principal Component Analysis Using Statistical Estimators | cs.AI | Principal Component Analysis (PCA) finds a linear mapping and maximizes the
variance of the data which makes PCA sensitive to outliers and may cause wrong
eigendirection. In this paper, we propose techniques to solve this problem; we
use the data-centering method and reestimate the covariance matrix using robust
statis... | computer science |
19,753 | Higher-Order Partial Least Squares (HOPLS): A Generalized Multi-Linear
Regression Method | cs.AI | A new generalized multilinear regression model, termed the Higher-Order
Partial Least Squares (HOPLS), is introduced with the aim to predict a tensor
(multiway array) $\tensor{Y}$ from a tensor $\tensor{X}$ through projecting the
data onto the latent space and performing regression on the corresponding
latent variables... | computer science |
19,754 | Cost Sensitive Reachability Heuristics for Handling State Uncertainty | cs.AI | While POMDPs provide a general platform for non-deterministic conditional
planning under a variety of quality metrics they have limited scalability. On
the other hand, non-deterministic conditional planners scale very well, but
many lack the ability to optimize plan quality metrics. We present a novel
generalization of... | computer science |
19,755 | Stable Independence in Perfect Maps | cs.AI | With the aid of the concept of stable independence we can construct, in an
efficient way, a compact representation of a semi-graphoid independence
relation. We show that this representation provides a new necessary condition
for the existence of a directed perfect map for the relation. The test for this
condition is ba... | computer science |
19,756 | 'Say EM' for Selecting Probabilistic Models for Logical Sequences | cs.AI | Many real world sequences such as protein secondary structures or shell logs
exhibit a rich internal structures. Traditional probabilistic models of
sequences, however, consider sequences of flat symbols only. Logical hidden
Markov models have been proposed as one solution. They deal with logical
sequences, i.e., seque... | computer science |
19,757 | Of Starships and Klingons: Bayesian Logic for the 23rd Century | cs.AI | Intelligent systems in an open world must reason about many interacting
entities related to each other in diverse ways and having uncertain features
and relationships. Traditional probabilistic languages lack the expressive
power to handle relational domains. Classical first-order logic is sufficiently
expressive, but ... | computer science |
19,758 | A Differential Semantics of Lazy AR Propagation | cs.AI | In this paper we present a differential semantics of Lazy AR Propagation
(LARP) in discrete Bayesian networks. We describe how both single and multi
dimensional partial derivatives of the evidence may easily be calculated from a
junction tree in LARP equilibrium. We show that the simplicity of the
calculations stems fr... | computer science |
19,759 | Modifying Bayesian Networks by Probability Constraints | cs.AI | This paper deals with the following problem: modify a Bayesian network to
satisfy a given set of probability constraints by only change its conditional
probability tables, and the probability distribution of the resulting network
should be as close as possible to that of the original network. We propose to
solve this p... | computer science |
19,760 | Exploiting Evidence-dependent Sensitivity Bounds | cs.AI | Studying the effects of one-way variation of any number of parameters on any
number of output probabilities quickly becomes infeasible in practice,
especially if various evidence profiles are to be taken into consideration. To
provide for identifying the parameters that have a potentially large effect
prior to actually... | computer science |
19,761 | MAA*: A Heuristic Search Algorithm for Solving Decentralized POMDPs | cs.AI | We present multi-agent A* (MAA*), the first complete and optimal heuristic
search algorithm for solving decentralized partially-observable Markov decision
problems (DEC-POMDPs) with finite horizon. The algorithm is suitable for
computing optimal plans for a cooperative group of agents that operate in a
stochastic envir... | computer science |
19,762 | A unified setting for inference and decision: An argumentation-based
approach | cs.AI | Inferring from inconsistency and making decisions are two problems which have
always been treated separately by researchers in Artificial Intelligence.
Consequently, different models have been proposed for each category. Different
argumentation systems [2, 7, 10, 11] have been developed for handling
inconsistency in kn... | computer science |
19,763 | Hybrid Bayesian Networks with Linear Deterministic Variables | cs.AI | When a hybrid Bayesian network has conditionally deterministic variables with
continuous parents, the joint density function for the continuous variables
does not exist. Conditional linear Gaussian distributions can handle such cases
when the continuous variables have a multi-variate normal distribution and the
discret... | computer science |
19,764 | On Bayesian Network Approximation by Edge Deletion | cs.AI | We consider the problem of deleting edges from a Bayesian network for the
purpose of simplifying models in probabilistic inference. In particular, we
propose a new method for deleting network edges, which is based on the evidence
at hand. We provide some interesting bounds on the KL-divergence between
original and appr... | computer science |
19,765 | Exploiting Evidence in Probabilistic Inference | cs.AI | We define the notion of compiling a Bayesian network with evidence and
provide a specific approach for evidence-based compilation, which makes use of
logical processing. The approach is practical and advantageous in a number of
application areas-including maximum likelihood estimation, sensitivity
analysis, and MAP com... | computer science |
19,766 | Use of Dempster-Shafer Conflict Metric to Detect Interpretation
Inconsistency | cs.AI | A model of the world built from sensor data may be incorrect even if the
sensors are functioning correctly. Possible causes include the use of
inappropriate sensors (e.g. a laser looking through glass walls), sensor
inaccuracies accumulate (e.g. localization errors), the a priori models are
wrong, or the internal repre... | computer science |
19,767 | Nonparametric Bayesian Logic | cs.AI | The Bayesian Logic (BLOG) language was recently developed for defining
first-order probability models over worlds with unknown numbers of objects. It
handles important problems in AI, including data association and population
estimation. This paper extends BLOG by adopting generative processes over
function spaces - kn... | computer science |
19,768 | Efficient algorithm for estimation of qualitative expected utility in
possibilistic case-based reasoning | cs.AI | We propose an efficient algorithm for estimation of possibility based
qualitative expected utility. It is useful for decision making mechanisms where
each possible decision is assigned a multi-attribute possibility distribution.
The computational complexity of ordinary methods calculating the expected
utility based on ... | computer science |
19,769 | Local Markov Property for Models Satisfying Composition Axiom | cs.AI | The local Markov condition for a DAG to be an independence map of a
probability distribution is well known. For DAGs with latent variables,
represented as bi-directed edges in the graph, the local Markov property may
invoke exponential number of conditional independencies. This paper shows that
the number of conditiona... | computer science |
19,770 | Unsupervised Activity Discovery and Characterization From Event-Streams | cs.AI | We present a framework to discover and characterize different classes of
everyday activities from event-streams. We begin by representing activities as
bags of event n-grams. This allows us to analyze the global structural
information of activities, using their local event statistics. We demonstrate
how maximal cliques... | computer science |
19,771 | Modeling Transportation Routines using Hybrid Dynamic Mixed Networks | cs.AI | This paper describes a general framework called Hybrid Dynamic Mixed Networks
(HDMNs) which are Hybrid Dynamic Bayesian Networks that allow representation of
discrete deterministic information in the form of constraints. We propose
approximate inference algorithms that integrate and adjust well known
algorithmic princi... | computer science |
19,772 | Approximate Inference Algorithms for Hybrid Bayesian Networks with
Discrete Constraints | cs.AI | In this paper, we consider Hybrid Mixed Networks (HMN) which are Hybrid
Bayesian Networks that allow discrete deterministic information to be modeled
explicitly in the form of constraints. We present two approximate inference
algorithms for HMNs that integrate and adjust well known algorithmic principles
such as Genera... | computer science |
19,773 | Metrics for Markov Decision Processes with Infinite State Spaces | cs.AI | We present metrics for measuring state similarity in Markov decision
processes (MDPs) with infinitely many states, including MDPs with continuous
state spaces. Such metrics provide a stable quantitative analogue of the notion
of bisimulation for MDPs, and are suitable for use in MDP approximation. We
show that the opti... | computer science |
19,774 | Existence and Finiteness Conditions for Risk-Sensitive Planning: Results
and Conjectures | cs.AI | Decision-theoretic planning with risk-sensitive planning objectives is
important for building autonomous agents or decision-support systems for
real-world applications. However, this line of research has been largely
ignored in the artificial intelligence and operations research communities
since planning with risk-sen... | computer science |
19,775 | Near-optimal Nonmyopic Value of Information in Graphical Models | cs.AI | A fundamental issue in real-world systems, such as sensor networks, is the
selection of observations which most effectively reduce uncertainty. More
specifically, we address the long standing problem of nonmyopically selecting
the most informative subset of variables in a graphical model. We present the
first efficient... | computer science |
19,776 | A Revision-Based Approach to Resolving Conflicting Information | cs.AI | In this paper, we propose a revision-based approach for conflict resolution
by generalizing the Disjunctive Maxi-Adjustment (DMA) approach (Benferhat et
al. 2004). Revision operators can be classified into two different families:
the model-based ones and the formula-based ones. So the revision-based approach
has two di... | computer science |
19,777 | Asynchronous Dynamic Bayesian Networks | cs.AI | Systems such as sensor networks and teams of autonomous robots consist of
multiple autonomous entities that interact with each other in a distributed,
asynchronous manner. These entities need to keep track of the state of the
system as it evolves. Asynchronous systems lead to special challenges for
monitoring, as nodes... | computer science |
19,778 | Expectation Propagation for Continuous Time Bayesian Networks | cs.AI | Continuous time Bayesian networks (CTBNs) describe structured stochastic
processes with finitely many states that evolve over continuous time. A CTBN is
a directed (possibly cyclic) dependency graph over a set of variables, each of
which represents a finite state continuous time Markov process whose transition
model is... | computer science |
19,779 | Expectation Maximization and Complex Duration Distributions for
Continuous Time Bayesian Networks | cs.AI | Continuous time Bayesian networks (CTBNs) describe structured stochastic
processes with finitely many states that evolve over continuous time. A CTBN is
a directed (possibly cyclic) dependency graph over a set of variables, each of
which represents a finite state continuous time Markov process whose transition
model is... | computer science |
19,780 | Sufficient conditions for convergence of Loopy Belief Propagation | cs.AI | We derive novel sufficient conditions for convergence of Loopy Belief
Propagation (also known as the Sum-Product algorithm) to a unique fixed point.
Our results improve upon previously known conditions. For binary variables with
(anti-)ferromagnetic interactions, our conditions seem to be sharp. | computer science |
19,781 | The Relationship Between AND/OR Search and Variable Elimination | cs.AI | In this paper we compare search and inference in graphical models through the
new framework of AND/OR search. Specifically, we compare Variable Elimination
(VE) and memoryintensive AND/OR Search (AO) and place algorithms such as
graph-based backjumping and no-good and good learning, as well as Recursive
Conditioning [7... | computer science |
19,782 | Representation Policy Iteration | cs.AI | This paper addresses a fundamental issue central to approximation methods for
solving large Markov decision processes (MDPs): how to automatically learn the
underlying representation for value function approximation? A novel
theoretically rigorous framework is proposed that automatically generates
geometrically customi... | computer science |
19,783 | Point-Based POMDP Algorithms: Improved Analysis and Implementation | cs.AI | Existing complexity bounds for point-based POMDP value iteration algorithms
focus either on the curse of dimensionality or the curse of history. We derive
a new bound that relies on both and uses the concept of discounted
reachability; our conclusions may help guide future algorithm design. We also
discuss recent impro... | computer science |
19,784 | Approximate Linear Programming for First-order MDPs | cs.AI | We introduce a new approximate solution technique for first-order Markov
decision processes (FOMDPs). Representing the value function linearly w.r.t. a
set of first-order basis functions, we compute suitable weights by casting the
corresponding optimization as a first-order linear program and show how
off-the-shelf the... | computer science |
19,785 | Predictive Linear-Gaussian Models of Stochastic Dynamical Systems | cs.AI | Models of dynamical systems based on predictive state representations (PSRs)
are defined strictly in terms of observable quantities, in contrast with
traditional models (such as Hidden Markov Models) that use latent variables or
statespace representations. In addition, PSRs have an effectively infinite
memory, allowing... | computer science |
19,786 | Efficient Test Selection in Active Diagnosis via Entropy Approximation | cs.AI | We consider the problem of diagnosing faults in a system represented by a
Bayesian network, where diagnosis corresponds to recovering the most likely
state of unobserved nodes given the outcomes of tests (observed nodes). Finding
an optimal subset of tests in this setting is intractable in general. We show
that it is d... | computer science |
19,787 | Importance Sampling in Bayesian Networks: An Influence-Based
Approximation Strategy for Importance Functions | cs.AI | One of the main problems of importance sampling in Bayesian networks is
representation of the importance function, which should ideally be as close as
possible to the posterior joint distribution. Typically, we represent an
importance function as a factorization, i.e., product of conditional
probability tables (CPTs). ... | computer science |
19,788 | Structured Region Graphs: Morphing EP into GBP | cs.AI | GBP and EP are two successful algorithms for approximate probabilistic
inference, which are based on different approximation strategies. An open
problem in both algorithms has been how to choose an appropriate approximation
structure. We introduce 'structured region graphs', a formalism which marries
these two strategi... | computer science |
19,789 | A Model for Reasoning with Uncertain Rules in Event Composition Systems | cs.AI | In recent years, there has been an increased need for the use of active
systems - systems required to act automatically based on events, or changes in
the environment. Such systems span many areas, from active databases to
applications that drive the core business processes of today's enterprises.
However, in many case... | computer science |
19,790 | Super-Mixed Multiple Attribute Group Decision Making Method Based on
Hybrid Fuzzy Grey Relation Approach Degree | cs.AI | The feature of our method different from other fuzzy grey relation method for
supermixed multiple attribute group decision-making is that all of the
subjective and objective weights are obtained by interval grey number and that
the group decisionmaking is performed based on the relative approach degree of
grey TOPSIS, ... | computer science |
19,791 | Generalized Hybrid Grey Relation Method for Multiple Attribute Mixed
Type Decision Making | cs.AI | The multiple attribute mixed type decision making is performed by four
methods, that is, the relative approach degree of grey TOPSIS method, the
relative approach degree of grey incidence, the relative membership degree of
grey incidence and the grey relation relative approach degree method using the
maximum entropy es... | computer science |
19,792 | The SeqBin Constraint Revisited | cs.AI | We revisit the SeqBin constraint. This meta-constraint subsumes a number of
important global constraints like Change, Smooth and IncreasingNValue. We show
that the previously proposed filtering algorithm for SeqBin has two drawbacks
even under strong restrictions: it does not detect bounds disentailment and it
is not i... | computer science |
19,793 | Arabic CALL system based on pedagogically indexed text | cs.AI | This article introduces the benefits of using computer as a tool for foreign
language teaching and learning. It describes the effect of using Natural
Language Processing (NLP) tools for learning Arabic. The technique explored in
this particular case is the employment of pedagogically indexed corpora. This
text-based me... | computer science |
19,794 | Etude de Modèles à base de réseaux Bayésiens pour l'aide au
diagnostic de tumeurs cérébrales | cs.AI | This article describes different models based on Bayesian networks RB
modeling expertise in the diagnosis of brain tumors. Indeed, they are well
adapted to the representation of the uncertainty in the process of diagnosis of
these tumors. In our work, we first tested several structures derived from the
Bayesian network... | computer science |
19,795 | Novel Grey Interval Weight Determining and Hybrid Grey Interval Relation
Method in Multiple Attribute Decision-Making | cs.AI | This paper proposes a grey interval relation TOPSIS for the decision making
in which all of the attribute weights and attribute values are given by the
interval grey numbers. The feature of our method different from other grey
relation decision-making is that all of the subjective and objective weights
are obtained by ... | computer science |
19,796 | Probabilistic Event Calculus for Event Recognition | cs.AI | Symbolic event recognition systems have been successfully applied to a
variety of application domains, extracting useful information in the form of
events, allowing experts or other systems to monitor and respond when
significant events are recognised. In a typical event recognition application,
however, these systems ... | computer science |
19,797 | Classification of Approaches and Challenges of Frequent Subgraphs Mining
in Biological Networks | cs.AI | Understanding the structure and dynamics of biological networks is one of the
important challenges in system biology. In addition, increasing amount of
experimental data in biological networks necessitate the use of efficient
methods to analyze these huge amounts of data. Such methods require to
recognize common patter... | computer science |
19,798 | Hybrid Grey Interval Relation Decision-Making in Artistic Talent
Evaluation of Player | cs.AI | This paper proposes a grey interval relation TOPSIS method for the decision
making in which all of the attribute weights and attribute values are given by
the interval grey numbers. In this paper, all of the subjective and objective
weights are obtained by interval grey number and decision-making is based on
four metho... | computer science |
19,799 | Qualitative Approximate Behavior Composition | cs.AI | The behavior composition problem involves automatically building a controller
that is able to realize a desired, but unavailable, target system (e.g., a
house surveillance) by suitably coordinating a set of available components
(e.g., video cameras, blinds, lamps, a vacuum cleaner, phones, etc.) Previous
work has almos... | computer science |
19,800 | Reasoning about Agent Programs using ATL-like Logics | cs.AI | We propose a variant of Alternating-time Temporal Logic (ATL) grounded in the
agents' operational know-how, as defined by their libraries of abstract plans.
Inspired by ATLES, a variant itself of ATL, it is possible in our logic to
explicitly refer to "rational" strategies for agents developed under the
Belief-Desire-I... | computer science |
19,801 | Exploiting First-Order Regression in Inductive Policy Selection | cs.AI | We consider the problem of computing optimal generalised policies for
relational Markov decision processes. We describe an approach combining some of
the benefits of purely inductive techniques with those of symbolic dynamic
programming methods. The latter reason about the optimal value function using
first-order decis... | computer science |
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