Conveners
Dynamic Decision Making
- Jaromír Kukal (FNSPE CTU in Prague)
The research studies optimal decision-making with the focus on preferences quantified for fully probabilistic design (FPD). FPD models the closed DM loop and the agent’s preferences by joint probability densities (pds). There is a preference-elicitation (PE) principle, which maps the agent’s model of the state transitions and its incompletely expressed wishes on an ideal pd quantifying them....
Modern decision making (DM) theory stands on classical probability. But there seems to be a variety of situations when the decision theory fails to explain some psychological and cognitive effects observed in human decision making.
Other aspects not covered by the classical approach are that the results of merging information depend on the order of merging, or that the observation influences...
Decision making (DM) is one of the key challenges since it appears in fields of study across all disciplines. The Fully Probabilistic Design of decision policies (FPD) represents an extension of well established decision making method using Markov Decision Processes (MDP). Both of these theories studies and try to model the evolution of states in the closed-loop through studying of transition...
Deep reinforcement learning has shown an ability to achieve super-human performance in solving complex reinforcement learning tasks only from raw-pixels. However, it fails to reuse knowledge from previously learnt tasks to solve new, unseen ones. To generalize and reuse knowledge is one of the fundamental requirements for creating a truly intelligent agent. The work summarizes the problem of...
