• The Transactions of the Korea Information Processing Society
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• v.4 no.1
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• pp.161-166
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• 1997

The blocks world is the primary problem domain in the artificial intelligence. Many planners have been developed in domain-independent. Gupta de fined the deadlock in the blocks world, and he proved that decision making in t he blocks world is NP-Complete problem. In this paper the properties of the dea dlock are analyzed, and a heuristic algorithms which can handle the deadlock of the Gupta's nondeterministic algorithm is proposed.

• International Journal of Control, Automation, and Systems
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• v.6 no.5
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• pp.639-650
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• 2008

Electricity price forecasting has become an integral part of power system operation and control. In this paper, a wavelet transform (WT) based neural network (NN) model to forecast price profile in a deregulated electricity market has been presented. The historical price data has been decomposed into wavelet domain constitutive sub series using WT and then combined with the other time domain variables to form the set of input variables for the proposed forecasting model. The behavior of the wavelet domain constitutive series has been studied based on statistical analysis. It has been observed that forecasting accuracy can be improved by the use of WT in a forecasting model. Multi-scale analysis from one to seven levels of decomposition has been performed and the empirical evidence suggests that accuracy improvement is highest at third level of decomposition. Forecasting performance of the proposed model has been compared with (i) a heuristic technique, (ii) a simulation model used by Ontario's Independent Electricity System Operator (IESO), (iii) a Multiple Linear Regression (MLR) model, (iv) NN model, (v) Auto Regressive Integrated Moving Average (ARIMA) model, (vi) Dynamic Regression (DR) model, and (vii) Transfer Function (TF) model. Forecasting results show that the performance of the proposed WT based NN model is satisfactory and it can be used by the participants to respond properly as it predicts price before closing of window for submission of initial bids.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.3
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• pp.27-37
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• 2021

Highly informative heuristics in AI planning can help to a more efficient search a solutions. However, in general, to obtain informative heuristics from planning problem specifications requires a lot of computational effort. To address this problem, we propose a Partial Planning Graph(PPG) and Mixed Heuristics for solving planning problems more efficiently. The PPG is an improved graph to be applied to can find a partial heuristic value for each goal condition from the relaxed planning graph which is a means to get heuristics to solve planning problems. Mixed Heuristics using PPG requires size of each graph is relatively small and less computational effort as a partial plan generated for each goal condition compared to the existing planning graph. Mixed Heuristics using PPG can find partial interactions for each goal conditions in an effective way, then consider them in order to estimate the goal state heuristics. Therefore Mixed Heuristics can not only find interactions for each goal conditions more less computational effort, but also have high accuracy of heuristics than the existing max and additive heuristics. In this paper, we present the PPG and the algorithm for computing Mixed Heuristics, and then explain analysis to accuracy and the efficiency of the Mixed Heuristics.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.5
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• pp.1233-1246
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• 2018

With the proliferation of smart mobiles, disassembly techniques for position-independent code (PIC) composed of ARM architecture instructions in computer security are becoming more important. However, existing techniques have been studied on x86 architecture and are focused on solving problems of non-PIC and generality. Therefore, the accuracy of the collected address information is low to apply to advanced security technologies such as binary measurement. In this paper, we propose a disassembly technique that reflects the characteristics of PIC composed of ARM instructions. For accuratly collecting traceable addresses, we designed value-set analysis having symbol-form domain. To solve the main problem of disassembly, we devised a heuristic using the characteristics of the code generated by the compiler. To verify the accuracy and effectiveness of our technique, we tested 669 shared libraries and executables in the Android 8.1 build, resulting in a total disassembly rate of 91.47%.

• The KIPS Transactions:PartB
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• v.16B no.6
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• pp.489-496
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• 2009

Hierarchical Task Network(HTN) planning, a typical planning method for effectively taking advantage of domain-specific control knowledge, has been widely used in complex real applications for a long time. However, it still lacks theoretical formalization and standardization, and so there are some differences among existing HTN planners in terms of principle and performance. In this paper, we present an effective way to translate a HTN planning domain specification into the corresponding standard PDDL specification. Its main advantage is to allow even many domain-independent classical planners to utilize domain-specific control knowledge contained in the HTN specifications. In this paper, we try our translation-based approach to three different domains such as Blocks World, Office Delivery, Hanoi Tower, and then conduct some experiments with a forward-chaining heuristic state-space planner, FF, to analyze the efficiency of our approach.

• The KIPS Transactions:PartB
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• v.18B no.1
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• pp.29-38
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• 2011

In order to derive domain-independent heuristics from the specification of a planning problem, it is required to relax the given problem and then solve the relaxed one. In this paper, we present a new planning graph, Merged Planning Graph(MPG), and GD heuristics for solving contingent planning problems with both uncertainty about the initial state and non-deterministic action effects. The merged planning graph is an extended one to be applied to the contingent planning problems from the relaxed planning graph, which is a common means to get effective heuristics for solving the classical planning problems. In order to get heuristics for solving the contingent planning problems with sensing actions and non-deterministic actions, the new graph utilizes additionally the effect-merge relaxations of these actions as well as the traditional delete relaxations. Proceeding parallel to the forward expansion of the merged planning graph, the computation of GD heuristic excludes the unnecessary redundant cost from estimating the minimal reachability cost to achieve the overall set of goals by analyzing interdependencies among goals or subgoals. Therefore, GD heuristics have the advantage that they usually require less computation time than the overlap heuristics, but are more informative than the max and the additive heuristics. In this paper, we explain the experimental analysis to show the accuracy and the search efficiency of the GD heuristics.