We propose and study a planning problem we call Sequential Fault-Intolerant Process Planning (SFIPP). SFIPP captures a reward structure common in many sequential multi-stage decision problems where the planning is deemed successful only if all stages succeed. Such reward structures are different from classic additive reward structures and arise in important applications such as drug/material discovery, security, and quality-critical product design. We design provably tight online algorithms for settings in which we need to pick between different actions with unknown success chances at each stage. We do so both for the foundational case in which the behavior of actions is deterministic, and the case of probabilistic action outcomes, where we effectively balance exploration for learning and exploitation for planning through the usage of multi-armed bandit algorithms. In our empirical evaluations, we demonstrate that the specialized algorithms we develop, which leverage additional information about the structure of the SFIPP instance, outperform our more general algorithm.

Where a collective decision over a set of options based on a number of opinions has to be reached, conflict is inevitable. Reflected by differences in the opinions, it usually comes from different perspectives of the opinions (e.g., in the case of human opinions, a beginner investor would likely have completely different opinion on various asset classes than their professional counterpart). However, conflict might also be option-based and stem from diverse, sometimes even contradicting, inherent qualities of the options (e.g., potentially high-return assets typically have high risk levels). We are interested in how to identify these conflicting options, based on the preferences. Since the options might represent multiple entities (e.g., sports players, societal issues, or marketing strategies), answering this question has numerous natural applications that include selecting competitors to organize engaging sport events (e.g., boxing matches), controversial topics to organize interesting political debates, disputable issues for socially-relevant deliberations, or conflicting ideas for boosting the creativity with passionate discussions.

We study the complexity of Destructive Shift Bribery. In this problem, we are given an election with a set of candidates and a set of voters (each ranking the candidates from the best to the worst), a despised candidate $d$, a budget $B$, and prices for shifting $d$ back in the voters' rankings. The goal is to ensure that $d$ is not a winner of the election. We show that this problem is polynomial-time solvable for scoring protocols (encoded in unary), the Bucklin and Simplified Bucklin rules, and the Maximin rule, but is NP-hard for the Copeland rule. This stands in contrast to the results for the constructive setting (known from the literature), for which the problem is polynomial-time solvable for $k$-Approval family of rules, but is NP-hard for the Borda, Copeland, and Maximin rules. We complement the analysis of the Copeland rule showing W-hardness for the parameterization by the budget value, and by the number of affected voters. We prove that the problem is W-hard when parameterized by the number of voters even for unit prices. From the positive perspective we provide an efficient algorithm for solving the problem parameterized by the combined parameter the number of candidates and the maximum bribery price (alternatively the number of different bribery prices).