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| Modello di Markov Multi-obiettivo× | Modello di Markov× | |
|---|---|---|
| Campo | Simulazione | Simulazione |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 2006 | 1906 |
| Ideatore≠ | Chatterjee, K., Majumdar, R., Henzinger, T. A. (formal; survey: Roijers et al.) | Andrei Markov |
| Tipo≠ | Stochastic sequential decision model with multiple objectives | Probabilistic state-transition model |
| 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 ↗ | Norris, J. R. (1997). Markov Chains. Cambridge University Press, Cambridge. ISBN: 9780521633963 |
| Alias | MOMDP, Multi-objective MDP, Multi-criteria Markov Decision Process, MO-Markov Model | Markov Chain, Discrete-Time Markov Chain, DTMC, Markov Process |
| 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. | A Markov Model represents a system as a finite set of states and specifies the probability of moving from one state to another at each time step. By capturing only the current state — not the full history — it enables tractable analysis of complex dynamic processes across health economics, engineering reliability, operations research, and social-science modeling. |
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