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| Modello di Markov Multi-obiettivo× | Programmazione Dinamica Multi-Obiettivo× | |
|---|---|---|
| Campo | Simulazione | Simulazione |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 2006 | 1957-1975 |
| Ideatore≠ | Chatterjee, K., Majumdar, R., Henzinger, T. A. (formal; survey: Roijers et al.) | Extension of Bellman (1957); formalized by multiple authors from 1970s onward |
| Tipo≠ | Stochastic sequential decision model with multiple objectives | Exact optimization — recursive multi-objective decomposition |
| Fonte seminale≠ | 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 |
| Alias | MOMDP, Multi-objective MDP, Multi-criteria Markov Decision Process, MO-Markov Model | MODP, Multi-criteria dynamic programming, Vector dynamic programming, Pareto dynamic programming |
| Correlati | 5 | 5 |
| Sintesi≠ | 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. |
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