In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

[Q]: As a general and thus popular model for autonomous systems, partially observable Markov decision process (POMDP) can capture uncertainties from different sources like sensing noises, actuation errors, and uncertain environments. However, its comprehensiveness makes the planning and control in POMDP difficult. Traditional POMDP planning problems target to find the optimal policy to maximize the expectation of accumulated rewards. But for safety critical applications, guarantees of system performance described by formal specifications are desired, which motivates us to consider formal methods to synthesize supervisor for POMDP. With system specifications given by Probabilistic Computation Tree Logic (PCTL), we propose a supervisory control framework with a type of deterministic finite automata (DFA), za-DFA, as the controller form. While the existing work mainly relies on optimization techniques to learn fixed-size finite state controllers (FSCs), we develop an L∗ learning based algorithm to determine both space and transitions of za-DFA. Membership queries and different oracles for conjectures are defined. The learning algorithm is sound and complete. An example is given in detailed steps to illustrate the supervisor synthesis algorithm.
[A]: Supervisor Synthesis of POMDP based on Automata Learning


[Q]: Online learning aims to perform nearly as well as the best hypothesis in hindsight. For some hypothesis classes, though, even finding the best hypothesis offline is challenging. In such offline cases, local search techniques are often employed and only local optimality guaranteed. For online decision-making with such hypothesis classes, we introduce local regret, a generalization of regret that aims to perform nearly as well as only nearby hypotheses. We then present a general algorithm to minimize local regret with arbitrary locality graphs. We also show how the graph structure can be exploited to drastically speed learning. These algorithms are then demonstrated on a diverse set of online problems: online disjunct learning, online Max-SAT, and online decision tree learning.
[A]: On Local Regret


[Q]: Collaborative human activities are grounded in social and moral norms, which humans consciously and subconsciously use to guide and constrain their behavior: they undergird human societies by prescribing what is obligatory, permitted, prohibited, and optional [1]. In doing so, they enable effective collaboration and prevent emotional and physical harm. Consider a restaurant kitchen where cooks and assistants perform tasks such as passing knives and cutting vegetables. When handing over a knife to the chef, assistants do so in a way that does not look like they are about to stab the chef. Not only will they orient the knife in the right way, but they should take care not to approach the chef menacingly and without prior warning, while the chef has their back to them. The underlying normative principle could be roughly stated as a rule: “If you need to hand over a potentially dangerous object with a sharp blade, do not point it with the blade at the other person, but rather grasp it carefully by the blade and hand it over with the bland side or handle facing the other person”. The tacit understanding among the kitchen staff is that everyone will abide by this principle, thus enabling safe exchanges of knives and other potentially dangerous objects. Failing to follow the rule will likely result in blame and reprimand from the chef, which then has to be addressed either by apologizing or by offering an explanation as to why the rule violation was justified [2]. Clearly, social and moral norms play an important functional role in the human cognitive architecture: they are at work in perception to detect morally charged contexts and norm violations, they are employed during decision-making and behavior execution, and they are referred to in communications about normative behavior and norm violations. In other words, normative processing is deeply integrated into the human cognitive system and affects virtually every aspect of the architecture (from perception, to reasoning, to action, to communication). Hence, this type of norm-based processing is also critical for robots in many human-robot interaction scenarios (e.g., when helping elderly and disabled persons in assisted living facilities, or assisting humans in assembly tasks in factories or even the space station). Human beings expect their interactants, including intelligent robots, to follow social and moral norms, and disappointing those expectations will lead to impoverished interactions at best, but can lead to emotional and physical harm in the worst cases. In this position paper, we will briefly describe how several components in an integrated cognitive architecture can be used to implement processes that are required for normative human-robot interactions, especially in collaborative tasks where actions and situations could potentially be perceived as threatening and thus need a change in course of action to mitigate the perceived threats. We will focus on affordancebased reasoning to infer complex affordance relationships between an agent and objects in the environment, and analogical reasoning to decide the appropriateness of the action plan by comparing against other past situations.
[A]:
Enabling Basic Normative HRI in a Cognitive Robotic Architecture