AI Access Foundation

Displaying 1-17 of 17 results

  • White Papers // Feb 2013

    Generating Extractive Summaries of Scientific Paradigms

    Researchers and scientists increasingly find themselves in the position of having to quickly understand large amounts of technical material. The authors' goal is to effectively serve this need by using bibliometric text mining and summarization techniques to generate summaries of scientific literature. They show how they can use citations to...

    Provided By AI Access Foundation

  • White Papers // Feb 2013

    Integrative Semantic Dependency Parsing via Efficient Large-scale Feature Selection

    Semantic parsing, i.e., the automatic derivation of meaning representation such as an instantiated predicate-argument structure for a sentence, plays a critical role in deep processing of natural language. Unlike all other top systems of semantic dependency parsing that have to rely on a pipeline framework to chain up a series...

    Provided By AI Access Foundation

  • White Papers // Feb 2013

    Toward Supervised Anomaly Detection

    Anomaly detection is being regarded as an unsupervised learning task as anomalies stem from adversarial or unlikely events with unknown distributions. However, the predictive performance of purely unsupervised anomaly detection often fails to match the required detection rates in many tasks and there exists a need for labeled data to...

    Provided By AI Access Foundation

  • White Papers // Jan 2013

    Generative Prior Knowledge for Discriminative Classification

    The authors present a novel framework for integrating prior knowledge into discriminative classifiers. Their 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 formulated as a bilevel program,...

    Provided By AI Access Foundation

  • White Papers // Jan 2013

    Properties and Applications of Programs with Monotone and Convex Constraints

    The authors study properties of programs with monotone and convex constraints. They extend to these formalisms concepts and results from normal logic programming. They include the notions of strong and uniform equivalence with their characterizations, tight programs and Fages Lemma, program completion and loop formulas. Their results provide an abstract...

    Provided By AI Access Foundation

  • White Papers // Jan 2013

    Understanding Algorithm Performance on an Oversubscribed Scheduling Application

    The best performing algorithms for a particular oversubscribed scheduling application, Air Force Satellite Control Network (AFSCN) scheduling, appear to have little in common. Yet, through careful experimentation and modeling of performance in real problem instances, the authors can relate characteristics of the best algorithms to characteristics of the application. In...

    Provided By AI Access Foundation

  • White Papers // Jan 2013

    Short and Long Supports for Constraint Propagation

    Special-purpose constraint propagation algorithms frequently make implicit use of short supports - by examining a subset of the variables, they can infer support (a justification that a variable-value pair may still form part of an assignment that satisfies the constraint) for all other variables and values and save substantial work...

    Provided By AI Access Foundation

  • White Papers // Jan 2013

    Optimal Rectangle Packing: An Absolute Placement Approach

    The authors consider the problem of finding all enclosing rectangles of minimum area that can contain a given set of rectangles without overlap. Their rectangle packer chooses the x-coordinates of all the rectangles before any of the y-coordinates. They then transform the problem into a perfect-packing problem with no empty...

    Provided By AI Access Foundation

  • White Papers // Jan 2013

    Automatic Aggregation by Joint Modeling of Aspects and Values

    The authors present a model for aggregation of product review snippets by joint aspect identification and sentiment analysis. Their model simultaneously identifies an underlying set of ratable aspects presented in the reviews of a product (e.g., sushi and miso for a Japanese restaurant) and determines the corresponding sentiment of each...

    Provided By AI Access Foundation

  • White Papers // Jan 2013

    Undominated Groves Mechanisms

    The family of Groves mechanisms, which includes the well-known VCG mechanism (also known as the Clarke mechanism) is a family of efficient and strategy-proof mechanisms. Unfortunately, the Groves mechanisms are generally not budget balanced. That is, under such mechanisms, payments may flow into or out of the system of the...

    Provided By AI Access Foundation

  • White Papers // Jan 2013

    Asynchronous Partial Overlay: A New Algorithm for Solving Distributed Constraint Satisfaction Problems

    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 paper, the authors present a new complete, distributed algorithm called Asynchronous...

    Provided By AI Access Foundation

  • White Papers // Jan 2013

    The Fast Downward Planning System

    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 FF, fast downward is...

    Provided By AI Access Foundation

  • White Papers // Oct 2012

    Transforming Graph Data for Statistical Relational Learning

    Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of Statistical Relational Learning (SRL) algorithms to these domains. In this paper, the authors examine and categorize techniques for transforming graph-based...

    Provided By AI Access Foundation

  • White Papers // Sep 2012

    Towards Unsupervised Learning of Temporal Relations Between Events

    Automatic extraction of temporal relations between event pairs is an important task for several natural language processing applications such as question answering, information extraction and summarization. Since most existing methods are supervised and require large corpora, which for many languages do not exist, the authors have concentrated their efforts to...

    Provided By AI Access Foundation

  • White Papers // Sep 2012

    Interactions Between Knowledge and Time in a First-Order Logic for Multi-Agent Systems: Completeness Results

    While reactive systems (Pnueli, 1977) are traditionally specified using plain temporal logic, there is a well-established tradition in Artificial Intelligence (AI) and, in particular, Multi-Agent Systems (MAS) research to adopt more expressive languages. The authors investigate a class of first-order temporal-epistemic logics for reasoning about multi-agent systems. They encode typical...

    Provided By AI Access Foundation

  • White Papers // Sep 2012

    The Tractability of CSP Classes Defined by Forbidden Patterns

    The Constraint Satisfaction Problem (CSP) is a general problem central to computer science and artificial intelligence. Although the CSP is NP-hard in general, considerable effort has been spent on identifying tractable subclasses. The main two approaches consider structural properties (restrictions on the hypergraph of constraint scopes) and relational properties (restrictions...

    Provided By AI Access Foundation

  • White Papers // Oct 2011

    On the Formal Semantics of Speech-Act Based Communication in an Agent-Oriented Programming Language

    Research on agent communication languages has typically taken the speech acts paradigm as its starting point. Despite their manifest attractions, speech-act models of communication have several serious disadvantages as a foundation for communication in artificial agent systems. In particular, it has proved to be extremely difficult to give a satisfactory...

    Provided By AI Access Foundation

  • White Papers // Sep 2012

    Interactions Between Knowledge and Time in a First-Order Logic for Multi-Agent Systems: Completeness Results

    While reactive systems (Pnueli, 1977) are traditionally specified using plain temporal logic, there is a well-established tradition in Artificial Intelligence (AI) and, in particular, Multi-Agent Systems (MAS) research to adopt more expressive languages. The authors investigate a class of first-order temporal-epistemic logics for reasoning about multi-agent systems. They encode typical...

    Provided By AI Access Foundation

  • White Papers // Sep 2012

    The Tractability of CSP Classes Defined by Forbidden Patterns

    The Constraint Satisfaction Problem (CSP) is a general problem central to computer science and artificial intelligence. Although the CSP is NP-hard in general, considerable effort has been spent on identifying tractable subclasses. The main two approaches consider structural properties (restrictions on the hypergraph of constraint scopes) and relational properties (restrictions...

    Provided By AI Access Foundation

  • White Papers // Sep 2012

    Towards Unsupervised Learning of Temporal Relations Between Events

    Automatic extraction of temporal relations between event pairs is an important task for several natural language processing applications such as question answering, information extraction and summarization. Since most existing methods are supervised and require large corpora, which for many languages do not exist, the authors have concentrated their efforts to...

    Provided By AI Access Foundation

  • White Papers // Oct 2012

    Transforming Graph Data for Statistical Relational Learning

    Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of Statistical Relational Learning (SRL) algorithms to these domains. In this paper, the authors examine and categorize techniques for transforming graph-based...

    Provided By AI Access Foundation

  • White Papers // Oct 2011

    On the Formal Semantics of Speech-Act Based Communication in an Agent-Oriented Programming Language

    Research on agent communication languages has typically taken the speech acts paradigm as its starting point. Despite their manifest attractions, speech-act models of communication have several serious disadvantages as a foundation for communication in artificial agent systems. In particular, it has proved to be extremely difficult to give a satisfactory...

    Provided By AI Access Foundation

  • White Papers // Jan 2013

    Generative Prior Knowledge for Discriminative Classification

    The authors present a novel framework for integrating prior knowledge into discriminative classifiers. Their 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 formulated as a bilevel program,...

    Provided By AI Access Foundation

  • White Papers // Jan 2013

    Properties and Applications of Programs with Monotone and Convex Constraints

    The authors study properties of programs with monotone and convex constraints. They extend to these formalisms concepts and results from normal logic programming. They include the notions of strong and uniform equivalence with their characterizations, tight programs and Fages Lemma, program completion and loop formulas. Their results provide an abstract...

    Provided By AI Access Foundation

  • White Papers // Jan 2013

    Understanding Algorithm Performance on an Oversubscribed Scheduling Application

    The best performing algorithms for a particular oversubscribed scheduling application, Air Force Satellite Control Network (AFSCN) scheduling, appear to have little in common. Yet, through careful experimentation and modeling of performance in real problem instances, the authors can relate characteristics of the best algorithms to characteristics of the application. In...

    Provided By AI Access Foundation

  • White Papers // Jan 2013

    Short and Long Supports for Constraint Propagation

    Special-purpose constraint propagation algorithms frequently make implicit use of short supports - by examining a subset of the variables, they can infer support (a justification that a variable-value pair may still form part of an assignment that satisfies the constraint) for all other variables and values and save substantial work...

    Provided By AI Access Foundation

  • White Papers // Jan 2013

    Optimal Rectangle Packing: An Absolute Placement Approach

    The authors consider the problem of finding all enclosing rectangles of minimum area that can contain a given set of rectangles without overlap. Their rectangle packer chooses the x-coordinates of all the rectangles before any of the y-coordinates. They then transform the problem into a perfect-packing problem with no empty...

    Provided By AI Access Foundation

  • White Papers // Jan 2013

    Automatic Aggregation by Joint Modeling of Aspects and Values

    The authors present a model for aggregation of product review snippets by joint aspect identification and sentiment analysis. Their model simultaneously identifies an underlying set of ratable aspects presented in the reviews of a product (e.g., sushi and miso for a Japanese restaurant) and determines the corresponding sentiment of each...

    Provided By AI Access Foundation

  • White Papers // Jan 2013

    Undominated Groves Mechanisms

    The family of Groves mechanisms, which includes the well-known VCG mechanism (also known as the Clarke mechanism) is a family of efficient and strategy-proof mechanisms. Unfortunately, the Groves mechanisms are generally not budget balanced. That is, under such mechanisms, payments may flow into or out of the system of the...

    Provided By AI Access Foundation

  • White Papers // Feb 2013

    Generating Extractive Summaries of Scientific Paradigms

    Researchers and scientists increasingly find themselves in the position of having to quickly understand large amounts of technical material. The authors' goal is to effectively serve this need by using bibliometric text mining and summarization techniques to generate summaries of scientific literature. They show how they can use citations to...

    Provided By AI Access Foundation

  • White Papers // Feb 2013

    Integrative Semantic Dependency Parsing via Efficient Large-scale Feature Selection

    Semantic parsing, i.e., the automatic derivation of meaning representation such as an instantiated predicate-argument structure for a sentence, plays a critical role in deep processing of natural language. Unlike all other top systems of semantic dependency parsing that have to rely on a pipeline framework to chain up a series...

    Provided By AI Access Foundation

  • White Papers // Feb 2013

    Toward Supervised Anomaly Detection

    Anomaly detection is being regarded as an unsupervised learning task as anomalies stem from adversarial or unlikely events with unknown distributions. However, the predictive performance of purely unsupervised anomaly detection often fails to match the required detection rates in many tasks and there exists a need for labeled data to...

    Provided By AI Access Foundation

  • White Papers // Jan 2013

    Asynchronous Partial Overlay: A New Algorithm for Solving Distributed Constraint Satisfaction Problems

    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 paper, the authors present a new complete, distributed algorithm called Asynchronous...

    Provided By AI Access Foundation

  • White Papers // Jan 2013

    The Fast Downward Planning System

    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 FF, fast downward is...

    Provided By AI Access Foundation