Recommender systems exploit interaction history to estimate user preference, having been
heavily used in a wide range of industry applications. However, static recommendation …

Much research in artificial intelligence is concerned with the development of autonomous
agents that can interact effectively with other agents. An important aspect of such agents is …

Abstract The idea of Artificial Intelligence for Social Good (henceforth AI4SG) is gaining
traction within information societies in general and the AI community in particular. It has the …

We introduce a new algorithm for multi-objective reinforcement learning (MORL) with linear
preferences, with the goal of enabling few-shot adaptation to new tasks. In MORL, the aim is …

Reinforcement learning (RL) is a powerful paradigm for sequential decision-making under
uncertainties, and most RL algorithms aim to maximize some numerical value which …

A general framework for constructing and using probabilistic models of complex systems that
would enable a computer to use available information for making decisions. Most tasks …

The past decade has seen a significant breakthrough in research on solving partially
observable Markov decision processes (POMDPs). Where past solvers could not scale …

Typical recommender systems adopt a static view of the recommendation process and treat
it as a prediction problem. We argue that it is more appropriate to view the problem of …

In this paper, we address the problem of suboptimal behavior during online partially
observable Markov decision process (POMDP) planning caused by time constraints on …

AI systems often rely on two key components: a specified goal or reward function and an
optimization algorithm to compute the optimal behavior for that goal. This approach is …