방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 다목적 마르코프 모델× | 다중 목표 동적 계획법× | |
|---|---|---|
| 분야 | 시뮬레이션 | 시뮬레이션 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 2006 | 1957-1975 |
| 창시자≠ | Chatterjee, K., Majumdar, R., Henzinger, T. A. (formal; survey: Roijers et al.) | Extension of Bellman (1957); formalized by multiple authors from 1970s onward |
| 유형≠ | Stochastic sequential decision model with multiple objectives | Exact optimization — recursive multi-objective decomposition |
| 원전≠ | Roijers, D. M., Vamplew, P., Whiteson, S., & Dazeley, R. (2013). A survey of multi-objective sequential decision-making. Journal of Artificial Intelligence Research, 48, 67–113. DOI ↗ | Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780691079516 |
| 별칭 | MOMDP, Multi-objective MDP, Multi-criteria Markov Decision Process, MO-Markov Model | MODP, Multi-criteria dynamic programming, Vector dynamic programming, Pareto dynamic programming |
| 관련 | 5 | 5 |
| 요약≠ | A Multi-objective Markov Model (MOMDP) extends classical Markov Decision Processes to settings where an agent must optimize several reward signals simultaneously. Instead of a single optimal policy, the model produces a Pareto-optimal set of policies, enabling decision-makers to navigate trade-offs between competing goals such as cost, risk, and throughput over time. | Multi-Objective Dynamic Programming (MODP) extends Bellman's classical dynamic programming to settings where a decision-maker must optimize several competing objectives simultaneously across a sequence of stages. Rather than a single optimal policy, it produces a Pareto-optimal set of policies — each representing a distinct trade-off profile — by propagating vector-valued value functions backward through the state space. |
| ScholarGate데이터셋 ↗ |
|
|