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Nilufer Onder

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3 papers

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AAAI Conference 2013 Conference Paper

A First-Order Logic Based Framework for Verifying Simulations

Hui Meen Nyew
Nilufer Onder
Soner Onder
Zhenlin Wang

Modern science relies on simulation techniques for understanding phenomenon, exploring design options, or evaluating models. Assuring the correctness of simulators is a key problem where a multitude of solutions ranging from manual inspection to formal verification are applicable. Formal verification incorporates the rigor necessary but not all simulators are generated from formal specifications. Manual inspection is readily available but lacks the rigor and is prone to errors. In this paper, we describe an automated verification system (AVS) where the constraints that the system must adhere to are specified by the user in general purpose first-order logic. AVS translates these constraints into a verification program that scans the simulator trace and verifies that no constraints are violated. Computer microarchitecture simulations were successfully used to demonstrate the proposed approach. This paper describes the preliminary results and discusses how artificial intelligence techniques can be used to facilitate effective run-time verification of simulators.

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ICAPS Conference 2010 Conference Paper

Construction Management Applications: Challenges in Developing Execution Control Plans

Nilufer Onder
Amlan Mukherjee
Pei Tang

The objective of automated planners is to synthesize sequences of actions (called policies in MDP frameworks) that will achieve a predetermined goal given a fully or partially observable formal representation of the domain. In contrast, the main characteristic of project management is the greater emphasis on plan execution under uncertainty as opposed to plan synthesis. This paper explains the need to transition from automated plan synthesis to plan management and identifies the challenges for the planning and scheduling communities using examples of construction projects.

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AAAI Conference 1999 Conference Paper

Conditional, Probabilistic Planning: A Unifying Algorithm and Effective Search Control Mechanisms

Nilufer Onder
Martha E. Pollack
University of Pittsburgh

Several recent papers describe algorithms for generating conditional and/or probabilistic plans. In this paper, we synthesize this work, and present a unifying algorithm that incorporates andclarifies the main techniques that have been developed in the previous literature. Ouralgorithm decouplesthe search-control strategy for conditional and/or probabilistic planning from the underlying plan-refinementprocess. A similar decouplinghas provento be very useful in the analysis of classical planning algorithms, and weshowthat it can be at least as useful here, wherethe search-control decisions are even morecrucial. Previous probabilistic/conditional planners havebeenseverely limited by the fact that they do not knowhowto handle failure points to advantage. Weshowhowa principled selection of failure points can be performedwithin the frameworkour algorithm. Wealso describe and show the effectiveness of additional heuristics. Wedescribe our implemented system called Mahinurand experimentally demonstrate that our methodsproduce efficiency improvements of several orders of magnitude.

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