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Physical Sciences and Mathematics Commons™
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- Reinforcement Learning (RL) (1)
- Abstraction (1)
- Algorithms (1)
- Alias analysis (1)
- Bayesian learning (1)
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- Classes and objects (1)
- Clustering (1)
- Entropy (1)
- Heuristic search (1)
- Information retrieval; Clustering; Document visualization; User study (1)
- Languages (1)
- Markov chains (1)
- Measurement (1)
- Method invocation (1)
- Mobile robots (1)
- Object orientation (1)
- Performance (1)
- Polymorphism (1)
- Redundancy elimination (1)
- Subgoals (1)
- Time series (1)
Articles 1 - 12 of 12
Full-Text Articles in Physical Sciences and Mathematics
Language Models For Hierarchical Summarization (Proposal For Dissertation), Dawn Lawrie
Language Models For Hierarchical Summarization (Proposal For Dissertation), Dawn Lawrie
Computer Science Department Faculty Publication Series
Hierarchies have long been used for organization, summarization, and access to information. In this proposal we define summarization in terms of a probabilistic language model and use the definition to explore new techniques for automatically generating topic hierarchies. One technique applies a graph-theoretic algorithm, which is an approximation of the Dominating Set Problem. Another technique uses an entropy-based approach to choose topic terms. Both techniques efficiently select terms according to a language model. We compare the new techniques to previous methods proposed for constructing topic hierarchies including subsumption and lexical hierarchies, as well as words found using TF.IDF. Our preliminary …
Using Types To Analyze And Optimize Object-Oriented Programs, Amer Diwan, Kathryn S. Mckinley, J. Eliot B. Moss
Using Types To Analyze And Optimize Object-Oriented Programs, Amer Diwan, Kathryn S. Mckinley, J. Eliot B. Moss
Computer Science Department Faculty Publication Series
Object-oriented programming languages provide many software engineering benefits, but these often come at a performance cost. Object-oriented programs make extensive use of method in- vocations and pointer dereferences, both of which are potentially costly on modern machines. We show how to use types to produce effective, yet simple, techniques that reduce the costs of these features in Modula-3, a statically typed, object-oriented language. Our compiler performs type-based alias analysis to disambiguate memory references. It uses the results of the type-based alias analysis to eliminate redundant memory references and to replace monomorphic method in- vocation sites with direct calls. Using limit, …
The Right Algorithm At The Right Time: Comparing Data Flow Analysis Algorithms For Finite State Verification, Jamieson M. Cobleigh
The Right Algorithm At The Right Time: Comparing Data Flow Analysis Algorithms For Finite State Verification, Jamieson M. Cobleigh
Computer Science Department Faculty Publication Series
Finite state verification is emerging as an important technology for proving properties about software. In our experience, we have found that analysts have different expectations at different times. When an analyst is in an exploratory mode, initially formulating and verifying properties, analyses usually find inconsistencies because of flaws in the properties or in the software artifacts being analyzed. Once an inconsistency is found, the analyst begins to operate in a fault finding mode, during which meaningful counter example traces are needed to help determine the cause of the inconsistency. Eventually systems become relatively stable, but still require re-verification as evolution …
Flavers: A Finite State Verification Technique For Software Systems, Jamieson M. Cobleigh
Flavers: A Finite State Verification Technique For Software Systems, Jamieson M. Cobleigh
Computer Science Department Faculty Publication Series
Software systems are increasing in size and complexity and, subsequently, are becoming ever more difficult to validate. Finite State Verification (FSV) has been gaining credibility and attention as an alternative to testing and to formal verification approaches based on theorem proving. There has recently been a great deal of excitement about the potential for FSV approaches to prove properties about hardware descriptions but, for the most part, these approaches do not scale adequately to handle the complexity usually found in software. In this paper, we describe an FSV approach that creates a compact and conservative, but imprecise, model of the …
Maintaining Mutual Consistency For Cached Web Objects, Bhuvan Urgaonkar, Anoop George Ninan, Mohammad Salimullah Raunak, Prashant Shenoy, Krithi Ramamritham
Maintaining Mutual Consistency For Cached Web Objects, Bhuvan Urgaonkar, Anoop George Ninan, Mohammad Salimullah Raunak, Prashant Shenoy, Krithi Ramamritham
Computer Science Department Faculty Publication Series
Existing web proxy caches employ cache consistency mechanisms to ensure that locally cached data is consistent with that at the server. In this paper, we argue that techniques for maintaining consistency of individual objects are not sufficient—a proxy should employ additional mechanisms to ensure that related web objects are mutually consistent with one another. We formally define the notion of mutual consistency and the semantics provided by a mutual consistency mechanism to end-users. We then present techniques for maintaining mutual consistency in the temporal and value domains. A novel aspect of our techniques is that they can adapt to the …
A Visual Query Language For Relational Knowledge Discovery, H. Blau
A Visual Query Language For Relational Knowledge Discovery, H. Blau
Computer Science Department Faculty Publication Series
QGRAPH is a visual query language for knowledge discovery in relational data. Using QGRAPH, a user can query and update relational data in ways that support data exploration, data transformation, and sampling. When combined with modeling algorithms, such as those developed in inductive logic programming and relational learning, the language assists analysis of relational data, such as data drawn fromtheWeb, chemical structure-activity relationships, and social networks. Several features distinguish QGRAPH from other query languages such as SQL and Datalog. It is a visual language, so its queries are annotated graphs that reflect potential structures within a database. QGRAPH treats objects, …
Bayesian Clustering By Dynamics, Marco Ramoni, Paola Sebastiani, Paul Cohen
Bayesian Clustering By Dynamics, Marco Ramoni, Paola Sebastiani, Paul Cohen
Computer Science Department Faculty Publication Series
This paper introduces a Bayesian method for clustering dynamic processes. The method models dynamics as Markov chains and then applies an agglomerative clustering procedure to discover the most probable set of clusters capturing different dynamics. To increase ef£ciency, the method uses an entropy-based heuristic search strategy. A controlled experiment suggests that the method is very accurate when applied to artificial time series in a broad range of conditions and, when applied to clustering sensor data from mobile robots, it produces clusters that are meaningful in the domain of application.
Automatic Discovery Of Subgoals In Reinforcement Learning Using Diverse Density, Amy Mcgovern, Andrew G. Barto
Automatic Discovery Of Subgoals In Reinforcement Learning Using Diverse Density, Amy Mcgovern, Andrew G. Barto
Computer Science Department Faculty Publication Series
This paper presents a method by which a reinforcement learning agent can automatically discover certain types of subgoals online. By creating useful new subgoals while learning, the agent is able to accelerate learning on the current task and to transfer its expertise to other, related tasks through the reuse of its ability to attain subgoals. The agent discovers subgoals based on commonalities across multiple paths to a solution. We cast the task of finding these commonalities as a multiple-instance learning problem and use the concept of diverse density to find solutions. We illustrate this approach using several gridworld tasks.
Evaluating Combinations Of Ranked Lists And Visualizations Of Inter-Document Similarity, James Allan
Evaluating Combinations Of Ranked Lists And Visualizations Of Inter-Document Similarity, James Allan
Computer Science Department Faculty Publication Series
We are interested in how ideas from document clustering can be used to improve the retrieval accuracy of ranked lists in interactive systems. In particular, we are interested in ways to evaluate the e€ectiveness of such systems to decide how they might best be constructed. In this study, we construct and evaluate systems that present the user with ranked lists and a visualization of inter-document similarities. We ®rst carry out a user study to evaluate the clustering/ranked list combination on instance-oriented retrieval, the task of the TREC-6 Interactive Track. We ®nd that although users generally prefer the combination, they are …
Evaluation Of A Novel Two-Step Server Selection Metric, Katrina M. Hanna, Nandini Natarajan, Brian Neil Levine
Evaluation Of A Novel Two-Step Server Selection Metric, Katrina M. Hanna, Nandini Natarajan, Brian Neil Levine
Computer Science Department Faculty Publication Series
Choosing the best-performing server for a particular client from a group of replicated proxies is a difficult task. We offer a novel, two-step technique for server selection that chooses a small subset of five servers, and isolates testing to that subset for ten days. We present an empirical evaluation of both our method and previously proposed metrics based on traces to 193 commercial proxies. We show that our technique performs better than any of the other metrics we studied — often one to two seconds better for a one-megabyte file — while requiring considerably less work over time. Metrics such …
The Agent-Based Approach: A New Direction For Computational Models Of Development, Matthew Schlesinger, Domenico Parisi
The Agent-Based Approach: A New Direction For Computational Models Of Development, Matthew Schlesinger, Domenico Parisi
Computer Science Department Faculty Publication Series
The agent-based approach emphasizes the importance of learning through organism-environment interaction. This approach is part of a recent trend in computational models of learning and development toward studying autonomous organisms that are embedded in virtual or real environments. In this paper we introduce the concepts of online and offline sampling and highlight the role of online sampling in agent-based models. After comparing the strengths of each approach for modeling particular developmental phenomena and research questions, we describe a recent agent-based model of infant causal perception. We conclude by discussing some of the present limitations of agent-based models and suggesting how …
Accelerating Reinforcement Learning Through The Discovery Of Useful Subgoals, Amy Mcgovern, Andrew G. Barto
Accelerating Reinforcement Learning Through The Discovery Of Useful Subgoals, Amy Mcgovern, Andrew G. Barto
Computer Science Department Faculty Publication Series
An ability to adjust to changing environments and unforeseen circumstances is likely to be an important component of a successful autonomous space robot. This paper shows how to augment reinforcement learning algorithms with a method for automatically discovering certain types of subgoals online. By creating useful new subgoals while learning, the agent is able to accelerate learning on a current task and to transfer its expertise to related tasks through the reuse of its ability to attain subgoals. Subgoals are created based on commonalities across multiple paths to a solution. We cast the task of finding these commonalities as a …