• 한국노년학
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• v.31 no.2
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• pp.381-400
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• 2011

The purpose of this study is to develop the scale on Selection, Optimization, Compensation(SOC) model as successful aging strategies of Korean Elderly. In first phase of the study, 64 pilot items were collected from researcher's indepth interviews with a purposive sample group of 24 elderly people(16 items) and original SOC scale(48 items). To analyze the factor structure and to verify the validity of the scale, 592 questionnaires collected from survey were divided randomly into 300 developmental samples and 292 validity samples. The items were examined exploratory with developmental samples and confirmatory factor analysis with developmental samples. Two factor analysis supported four factor structure of the SOC consisted of 20 items. Four factors are as follows: 'Elective Selection', 'Loss-Based Selection', 'Opimization', 'Compensation'. The cronbach's alpha estimate of the scale was .930. This scale of four factor model exhibited good fit, assessed by overall fit measure criteria(TLI=.939, CFI=.947, RMSEA=.058). The result of analysis by item response theory for SOC scale is satisfatory. Also, SOC scale was significantly related to the two successful aging scales for Korean elderly and life satisfation scale(SWLS). These results proved the validity of the scale.

• Journal of Korean Academy of Nursing
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• v.42 no.3
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• pp.311-321
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• 2012

Purpose: This study was designed to construct and test a structural equation modeling on specific domain health status and the Selection Optimization Compensation (SOC) strategy affecting successful aging in elderly people. Methods: The model construction was based on the SOC model by Baltes and Baltes. Interviews were done with 201 elderly people aged 65 or older. Interview contents included demographics, functional health status, emotional health status, social health status, SOC strategies, and successful aging. Data were analyzed using SPSS 15.0 and AMOS 7.0. Results: Model fit indices for the modified model were GFI=.93, CFI=.94, and RMSEA=.07. Three out of 7 paths were found to have a significant effect on successful aging in this final model. Functional health status had a direct and positive effect on successful aging. Emotional health status influenced successful aging through SOC strategies. Conclusion: This study suggests that interventions for improving functional health status and for strengthening SOC strategies are critical for successful aging. Continuous development of a variety of successful aging programs using SOC strategy is suggested.

• The Journal of the Korea Contents Association
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• v.9 no.10
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• pp.51-58
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• 2009

Reinforcement learning is a sub area of machine learning concerned with how an agent ought to take actions in an environment so as to maximize some notion of long-term reward. In the case of multi-agent, especially, which state space and action space gets very enormous in compared to single agent, so it needs to take most effective measure available select the action strategy for effective reinforcement learning. This paper proposes a multi-agent reinforcement learning model based on fuzzy inference system in order to improve learning collect speed and select an effective action in multi-agent. This paper verifies an effective action select strategy through evaluation tests based on Robocup Keepaway which is one of useful test-beds for multi-agent. Our proposed model can apply to evaluate efficiency of the various intelligent multi-agents and also can apply to strategy and tactics of robot soccer system.

• The KIPS Transactions:PartB
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• v.11B no.1
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• pp.77-82
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• 2004

Reinforcement learning takes reward about selecting action when agent chooses some action and did state transition in Present state. this can be the important subject in reinforcement learning as temporal-credit assignment problems. In this paper, by new meta heuristic method to solve hard combinational optimization problem, examine Ant-Q learning method that is proposed to solve Traveling Salesman Problem (TSP) to approach that is based for population that use positive feedback as well as greedy search. And, suggest Ant-TD reinforcement learning method that apply state transition through diversification strategy to this method and TD-error. We can show through experiments that the reinforcement learning method proposed in this Paper can find out an optimal solution faster than other reinforcement learning method like ACS and Ant-Q learning.