We consider Incentive Decision Processes, where a principal seeks to reduce its costs due to another agent's behavior, by offering incentives to the agent for alternate behavior. We focus on the case where a princ...
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ISBN:
(纸本)9780974903989
We consider Incentive Decision Processes, where a principal seeks to reduce its costs due to another agent's behavior, by offering incentives to the agent for alternate behavior. We focus on the case where a principal interacts with a greedy agent whose preferences are hidden and static. Though IDPs can be directly modeled as partially observable Markov decision processes (POMDP), we show that it is possible to directly reduce or approximate the IDP as a polynomiallysized MDP: when this representation is approximate, we prove the resulting policy is boundedly-optimal for the original IDP. Our empirical simulations demonstrate the performance benefit of our algorithms over simpler approaches, and also demonstrate that our approximate representation results in a significantly faster algorithm whose performance is extremely close to the optimal policy for the original IDP.
Time series prediction techniques have been shown to significantly reduce the radio use and energy consumption of wireless sensor nodes performing periodic data collection tasks. In this paper, we propose an implement...
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Least Square Support Vector machine (LS-SVM) converts the hinge loss function of SVM into a least square loss function which simplified the original quadratic programming training method to a linear system solving pro...
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String kernel-based machinelearning methods have yielded great success in practical tasks of struc- Tured/sequential data analysis. They often exhibit state-of-the-art performance on tasks such as docu- ment topic el...
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Recent advances in genome-wide identification of protein-protein interactions (PPIs) have produced an abundance of interaction data which give an insight into functional associations among proteins. However, it is kno...
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Many noise models do not faithfully reflect the noise processes introduced during data collection in many real-world applications. In particular, we argue that a type of noise referred to as sparse noise is quite comm...
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Intrusion Detection System (IDS) can handle intrusions in computer environments by triggering alerts to help the analysts for taking actions to stop the possible attack or intrusion. But, the IDS make the job of analy...
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Detection of intrusions in computer networks has been a growing problem motivating widespread research in computerscience to develop better Intrusion Detecting Systems (IDS). The existing IDS have been quite static a...
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Active participation of customers in the management of demand, and renewable energy supply, is a critical goal of the Smart Grid vision. However, this is a complex problem with numerous scenarios that are difficult to...
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Probabilistic planning captures the uncertainty of plan execution by probabilistically modeling the effects of actions in the environment, and therefore the probability of reaching different states from a given state ...
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ISBN:
(纸本)9781627480031
Probabilistic planning captures the uncertainty of plan execution by probabilistically modeling the effects of actions in the environment, and therefore the probability of reaching different states from a given state and action. In order to compute a solution for a probabilistic planning problem, planners need to manage the uncertainty associated with the different paths from the initial state to a goal state. Several approaches to manage uncertainty were proposed, e.g., consider all paths at once, perform determinization of actions, and sampling. In this paper, we introduce trajectory-based short-sighted Stochastic Shortest Path Problems (SSPs), a novel approach to manage uncertainty for probabilistic planning problems in which states reachable with low probability are substituted by artificial goals that heuristically estimate their cost to reach a goal state. We also extend the theoretical results of Short-Sighted Probabilistic Planner (SSiPP) [1] by proving that SSiPP always finishes and is asymptotically optimal under sufficient conditions on the structure of short-sighted SSPs. We empirically compare SSiPP using trajectorybased short-sighted SSPs with the winners of the previous probabilistic planning competitions and other state-of-the-art planners in the triangle tireworld problems. Trajectory-based SSiPP outperforms all the competitors and is the only planner able to scale up to problem number 60, a problem in which the optimal solution contains approximately 1070 states.
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