... {russell,stiller}@cs.berkeley.edu. 2. Lewis Stiller is currently at Franz Inc., 1995 Universi... more ... {russell,stiller}@cs.berkeley.edu. 2. Lewis Stiller is currently at Franz Inc., 1995 University Avenue, Berkeley, CA, 94704. stiller@franz.com. ... A restricted form of operator overloading has been implemented in the JPP preprocessor by Nik Shaylor. ...
DTS is a decision-theoretic scheduler, built on top of a flexible toolkit -- this paper focuses o... more DTS is a decision-theoretic scheduler, built on top of a flexible toolkit -- this paper focuses on how the toolkit might be reused in future NASA mission schedulers. The toolkit includes a user-customizable scheduling interface, and a 'Just-For-You' optimization engine. The customizable interface is built on two metaphors: objects and dynamic graphs. Objects help to structure problem specifications and related data, while dynamic graphs simplify the specification of graphical schedule editors (such as Gantt charts). The interface can be used with any 'back-end' scheduler, through dynamically-loaded code, interprocess communication, or a shared database. The 'Just-For-You' optimization engine includes user-specific utility functions, automatically compiled heuristic evaluations, and a postprocessing facility for enforcing scheduling policies. The optimization engine is based on BPS, the Bayesian Problem-Solver (1,2), which introduced a similar approach to solv...
This paper describes a decision theoretic scheduler (DTS) designed to employ state-of-the-art pro... more This paper describes a decision theoretic scheduler (DTS) designed to employ state-of-the-art probabilistic inference technology to speed the search for efficient solutions to constraint-satisfaction problems. Our approach involves assessing the performance of heuristic control strategies that are normally hard-coded into scheduling systems and using probabilistic inference to aggregate this information in light of the features of a given problem. The Bayesian Problem-Solver (BPS) introduced a similar approach to solving single agent and adversarial graph search patterns yielding orders-of-magnitude improvement over traditional techniques. Initial efforts suggest that similar improvements will be realizable when applied to typical constraint-satisfaction scheduling problems.
This dissertation describes several advances to the theory and practice of artificial intelligenc... more This dissertation describes several advances to the theory and practice of artificial intelligence scheduling and constraint-satisfaction techniques. I have developed and implemented these techniques during the construction of DTS, the Decision-Theoretic Scheduler, and its successor, SchedKit, a toolkit of scheduling algorithms and data structures. The dissertation describes and analyzes the three orthogonal approaches to improving a scheduler's performance. These are: (1) reducing the size of the state space to be searched, (2) reducing the per state cost of state generation and evaluation, and (3) reducing the number of states examined by selective search. To reduce the size of the state space. I have developed several new preprocessing algorithms designed to exploit resource constraints, including resource capacity and resource/task compatibility. Experiments show that it is possible to exploit resource capacity constraints efficiently despite their inherently disjunctive nat...
This paper describes DTS, a decisiontheoretic scheduler designed to employ stateof-the-art probab... more This paper describes DTS, a decisiontheoretic scheduler designed to employ stateof-the-art probabilistic inference technology to speed the search for efficient solutions to constraint-satisfaction problems. Our approach involves assessing the performance of heuristic control strategies that are normally hard-coded into scheduling systems, and using probabilistic inference to aggregate this information in light of features of a given problem. BPS, the Bayesian Problem-Solver [2], introduced a similar approach to solving singleagent and adversarial graph search problems, yielding orders-of-magnitude improvement over traditional techniques. Initial efforts suggest that similar improvements will be realizable when applied to typical constraint-satisfaction scheduling problems.
The RADAR project involves a collection of machine learning research thrusts that are integrated ... more The RADAR project involves a collection of machine learning research thrusts that are integrated into a cognitive personal assistant. Progress is examined with a test developed to measure the impact of learning when used by a human user. Three conditions ( ...
... {russell,stiller}@cs.berkeley.edu. 2. Lewis Stiller is currently at Franz Inc., 1995 Universi... more ... {russell,stiller}@cs.berkeley.edu. 2. Lewis Stiller is currently at Franz Inc., 1995 University Avenue, Berkeley, CA, 94704. stiller@franz.com. ... A restricted form of operator overloading has been implemented in the JPP preprocessor by Nik Shaylor. ...
DTS is a decision-theoretic scheduler, built on top of a flexible toolkit -- this paper focuses o... more DTS is a decision-theoretic scheduler, built on top of a flexible toolkit -- this paper focuses on how the toolkit might be reused in future NASA mission schedulers. The toolkit includes a user-customizable scheduling interface, and a 'Just-For-You' optimization engine. The customizable interface is built on two metaphors: objects and dynamic graphs. Objects help to structure problem specifications and related data, while dynamic graphs simplify the specification of graphical schedule editors (such as Gantt charts). The interface can be used with any 'back-end' scheduler, through dynamically-loaded code, interprocess communication, or a shared database. The 'Just-For-You' optimization engine includes user-specific utility functions, automatically compiled heuristic evaluations, and a postprocessing facility for enforcing scheduling policies. The optimization engine is based on BPS, the Bayesian Problem-Solver (1,2), which introduced a similar approach to solv...
This paper describes a decision theoretic scheduler (DTS) designed to employ state-of-the-art pro... more This paper describes a decision theoretic scheduler (DTS) designed to employ state-of-the-art probabilistic inference technology to speed the search for efficient solutions to constraint-satisfaction problems. Our approach involves assessing the performance of heuristic control strategies that are normally hard-coded into scheduling systems and using probabilistic inference to aggregate this information in light of the features of a given problem. The Bayesian Problem-Solver (BPS) introduced a similar approach to solving single agent and adversarial graph search patterns yielding orders-of-magnitude improvement over traditional techniques. Initial efforts suggest that similar improvements will be realizable when applied to typical constraint-satisfaction scheduling problems.
This dissertation describes several advances to the theory and practice of artificial intelligenc... more This dissertation describes several advances to the theory and practice of artificial intelligence scheduling and constraint-satisfaction techniques. I have developed and implemented these techniques during the construction of DTS, the Decision-Theoretic Scheduler, and its successor, SchedKit, a toolkit of scheduling algorithms and data structures. The dissertation describes and analyzes the three orthogonal approaches to improving a scheduler's performance. These are: (1) reducing the size of the state space to be searched, (2) reducing the per state cost of state generation and evaluation, and (3) reducing the number of states examined by selective search. To reduce the size of the state space. I have developed several new preprocessing algorithms designed to exploit resource constraints, including resource capacity and resource/task compatibility. Experiments show that it is possible to exploit resource capacity constraints efficiently despite their inherently disjunctive nat...
This paper describes DTS, a decisiontheoretic scheduler designed to employ stateof-the-art probab... more This paper describes DTS, a decisiontheoretic scheduler designed to employ stateof-the-art probabilistic inference technology to speed the search for efficient solutions to constraint-satisfaction problems. Our approach involves assessing the performance of heuristic control strategies that are normally hard-coded into scheduling systems, and using probabilistic inference to aggregate this information in light of features of a given problem. BPS, the Bayesian Problem-Solver [2], introduced a similar approach to solving singleagent and adversarial graph search problems, yielding orders-of-magnitude improvement over traditional techniques. Initial efforts suggest that similar improvements will be realizable when applied to typical constraint-satisfaction scheduling problems.
The RADAR project involves a collection of machine learning research thrusts that are integrated ... more The RADAR project involves a collection of machine learning research thrusts that are integrated into a cognitive personal assistant. Progress is examined with a test developed to measure the impact of learning when used by a human user. Three conditions ( ...
Uploads
Papers by Othar Hansson