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José Luis Ambite

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8 papers

2 author rows

TIST Journal 2022 Journal Article

Semi-Synchronous Federated Learning for Energy-Efficient Training and Accelerated Convergence in Cross-Silo Settings

Dimitris Stripelis
Paul M. Thompson
José Luis Ambite

There are situations where data relevant to machine learning problems are distributed across multiple locations that cannot share the data due to regulatory, competitiveness, or privacy reasons. Machine learning approaches that require data to be copied to a single location are hampered by the challenges of data sharing. Federated Learning (FL) is a promising approach to learn a joint model over all the available data across silos. In many cases, the sites participating in a federation have different data distributions and computational capabilities. In these heterogeneous environments existing approaches exhibit poor performance: synchronous FL protocols are communication efficient, but have slow learning convergence and high energy cost; conversely, asynchronous FL protocols have faster convergence with lower energy cost, but higher communication. In this work, we introduce a novel energy-efficient Semi-Synchronous Federated Learning protocol that mixes local models periodically with minimal idle time and fast convergence. We show through extensive experiments over established benchmark datasets in the computer-vision domain as well as in real-world biomedical settings that our approach significantly outperforms previous work in data and computationally heterogeneous environments.

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IJCAI Conference 2013 Conference Paper

Discovering Alignments in Ontologies of Linked Data

Rahul Parundekar
Craig A. Knoblock
José Luis Ambite

Recently, large amounts of data are being published using Semantic Web standards. Simultaneously, there has been a steady rise in links between objects from multiple sources. However, the ontologies behind these sources have remained largely disconnected, thereby challenging the interoperability goal of the Semantic Web. We address this problem by automatically ﬁnding alignments between concepts from multiple linked data sources. Instead of only considering the existing concepts in each ontology, we hypothesize new composite concepts, deﬁned using conjunctions and disjunctions of (RDF) types and value restrictions, and generate alignments between them. In addition, our techniques provide a novel method for curating the linked data web by pointing to likely incorrect or missing assertions. Our approach provides a deeper understanding of the relationships between linked data sources and increases the interoperability among previously disconnected ontologies.

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AAAI Conference 2002 Conference Paper

Getting from Here to There: Interactive Planning and Agent Execution for Optimizing Travel

José Luis Ambite
Craig A. Knoblock
and Jean Oh

Planning and monitoring a trip is a common but complicated human activity. Creating an itinerary is nontrivial because it requires coordination with existing schedules and making a variety of interdependent choices. Once planned, there are many possible events that can affect the plan, such as schedule changes or flight cancellations, and checking for these possible events requires time and effort. In this paper, we describe how Heracles and Theseus, two information gathering and monitoring tools that we built, can be used to simplify this process. Heracles is a hierarchical constraint planner that aids in interactive itinerary development by showing how a particular choice (e. g. , destination airport) affects other choices (e. g. , possible modes of transportation, available airlines, etc.). Heracles builds on an information agent platform, called Theseus, that provides the technology for efficiently executing agents for information gathering and monitoring tasks. In this paper we present the technologies underlying these systems and describe how they are applied to build a state-of-the-art travel system.

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AIJ Journal 2000 Journal Article

Flexible and scalable cost-based query planning in mediators: A transformational approach

José Luis Ambite
Craig A. Knoblock

The Internet provides access to a wealth of information. For any given topic or application domain there are a variety of available information sources. However, current systems, such as search engines or topic directories in the World Wide Web, offer only very limited capabilities for locating, combining, and organizing information. Mediators, systems that provide integrated access and database-like query capabilities to information distributed over heterogeneous sources, are critical to realize the full potential of meaningful access to networked information. Query planning, the task of generating a cost-efficient plan that computes a user query from the relevant information sources, is central to mediator systems. However, query planning is a computationally hard problem due to the large number of possible sources and possible orderings on the operations to process the data. Moreover, the choice of sources, data processing operations, and their ordering, strongly affects the plan cost. In this paper, we present an approach to query planning in mediators based on a general planning paradigm called Planning by Rewriting (PbR) (Ambite and Knoblock, 1997). Our work yields several contributions. First, our PbR-based query planner combines both the selection of the sources and the ordering of the operations into a single search space in which to optimize the plan quality. Second, by using local search techniques our planner explores the combined search space efficiently and produces high-quality plans. Third, because our query planner is an instantiation of a domain-independent framework it is very flexible and can be extended in a principled way. Fourth, our planner has an anytime behavior. Finally, we provide empirical results showing that our PbR-based query planner compares favorably on scalability and plan quality over previous approaches, which include both classical AI planning and dynamic-programming query optimization techniques.

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ICAPS Conference 2000 Conference Paper

Learning Plan Rewriting Rules

José Luis Ambite
Craig A. Knoblock
Steven Minton

Planning byRewriting (PbR) is a newparadigm forefficient high-quality plavningthat exploits plan rewriting rules and etficiel, t local search techniquesto tran~ form an easy-to-generate, butpossibly suboptimal, initial planintoa high-quality plan. Dcspitc theadvantages of PbRin termsof scalability, planquality, and anytime behavior, PbRrequires the user to define a set of domain-specific plan rewriting rules which can be difficult and time-consuming. This paper presents an approach to automatically learning the plaal rewriting rules basedon comparinginitial aJId oI>timai plans. Wereport results for several pla~nningdomains showingthat the learned rules are competitive with manually-specified ones, amdin several cases the lear. n~g zdgoritkmdiscovered novel rewriting rules.

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ICAPS Conference 1998 Conference Paper

Flexible and Scalable Query Planning in Distributed and Heterogeneous Environments

José Luis Ambite
Craig A. Knoblock

Wepresent the apphcationof the Planningby Rewriting (PbR)framework to queryplanningin distributed and heterogeneous environments. PbRis a new paradigmfor efficient high-qualityplanningthat exploits plan rewritingrules andefficient local search techniquesto transforman easy-to-generate, but possibly suboptimal, initial plan into a high-qualityplan. Theresulting planneris scalable, flexible, has anytime behavior, and, applied to queryplanning, yields a novel combinationof traditional query optimization with heterogeneous informationsourceselection. Queryplanners are the core componentof mediator systems, whichare becomingincreasingly important in a worldof interconnectedinformation, and constitute excellenttestbeds for planningtechnology.

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AAAI Conference 1997 Conference Paper

Planning by Rewriting: Efficiently Generating High-Quality Plans

José Luis Ambite

Domain-independent planning is a hard combinatorial problem. Taking into account plan quality makes the task even more difficult. We introduce a new paradigm for efficient high-quality planning that exploits plan rewriting rules and efficient, local search techniques to transform an easy-to-generate, but possibly suboptimal, initial plan into a low-cost plan. In addition to addressing the issues of efficiency and quality, this framework yields a new anytime planning algorithm. We have implemented this planner and applied it to several existing domains. The results show that this approach provides significant savings in planning effort while generating high-quality plans. be many plans that would solve the problem, so finding one is simple (that is, in polynomial time), but the cost of each solution varies greatly so that finding the optimal one may be difficult. We shall refer to these domains as optimization domains. Some optimization domains of great practical interest are query access planning and process planning. ’ Second, planning problems have a great deal of structure. Plans are a type of graphs with strong semantics, determined both by the general properties of planning and each particular domain specification. This structure should and can be exploited to improve the efficiency of the planning process.

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AAAI Conference 1996 Short Paper

Efficient Planning by Graph Rewriting

José Luis Ambite

Planning involves the generation of a network of actions that achieves a desired goal given an initial state of the world. There has been significant progress in the analysis of planning algorithms, particularly in partial-order and in hierarchical task network (HTN) planning (Kambhampati 95; Erol et al. 94). In this abstract we propose a more general framework in which planning is seen as a graph rewriting process. This approach subsumes previous work and offers new opportunities for efficient planning.

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