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Temporal logics over finite traces have recently seen wide application in a number of areas, from business process modelling, monitoring, and mining to planning and decision making. However, real-life dynamic systems contain a degree of uncertainty which ...

Logical query answering over Knowledge Graphs (KGs) is a fundamental yet complex task. A promising approach to achieve this is to embed queries and entities jointly into the same embedding space. Research along this line suggests that using multi-modal ...

Aleatoric Logic is the logic of dice, where Boolean propositions are replaced by independent probabilistic events. In a first order extension of this notion, Aleatoric predicates are applied to domain elements selected via independent probabilistic ...

Generative Datalog is an extension of Datalog that incorporates constructs for referencing parameterized probability distributions. This augmentation transforms the evaluation of a Generative Datalog program into a stochastic process, resulting in ...

Regression models are used in a wide range of applications providing a powerful scientific tool for researchers from different fields. Linear, or simple parametric, models are often not sufficient to describe complex relationships between input variables ...

We consider the problem of black-box multi-objective optimization (MOO) using expensive function evaluations (also referred to as experiments), where the goal is to approximate the true Pareto set of solutions by minimizing the total resource cost of ...

The healthcare system in China still has some defects such as the imbalance of medical resources. With the development of computer science, medical diagnostics digitisation has become possible. In this paper, a medical diagnosis system for jaundice based ...

Node order is one of the most important factors in learning the structure of a Bayesian network (BN) for probabilistic reasoning. To improve the BN structure learning, we propose a node order learning algorithm based on the frequently used ...

We propose a novel belief space planning technique for continuous dynamics by viewing the belief system as a hybrid dynamical system with time-driven switching. Our approach is based on the perturbation theory of differential equations and extends ...

Goal recognition is the problem of determining an agent's intent by observing its actions. In the context of AI research, the problem is tackled for two quite different purposes: to determine an agent's most probable goal or, for human-aware planning ...

In this paper we present Probabilistic Control Argumentation Frameworks (PCAFs) that extend classical Control Argumentation Frameworks (CAFs) to take into account probabilistic information in the reasoning process. We show that probabilities can be used ...

Recognising patterns that correlate multiple events over time becomes increasingly important in applications that exploit the Internet of Things, reaching from urban transportation through surveillance monitoring to business workflows. In many real-...

Probabilistic reasoning is an essential tool for robust decision-making systems because of its ability to explicitly handle real-world uncertainty, constraints and causal relations. Consequently, researchers are developing hybrid models by combining Deep ...

Several probabilistic extensions of description logic languages have been proposed and thoroughly studied. However, their practical use has been hampered by intractability of various reasoning tasks. While present-day knowledge bases (KBs) contain ...

To what extent does expertise depend on context? We observe professionals perform a task that is logically isomorphic to—but contextually distinct from—a familiar task in which they are skilled. We find that performance plummets when contextual cues ...

Games of chance and bluffing, such as bridge, The Resistance, and poker allow epistemic reasoning. Players know their own cards while being uncertain of opponents'. Success generally involves reducing your uncertainty without reducing that of your ...

Monitoring is an effective approach for identifying safety violations for complex cyber-physical systems. In this poster, we consider safety specifications expressed in Signal Temporal Logic (STL). STL is a logic for specifying timed properties of real-...

Monitoring is an effective approach for identifying safety violations for complex cyber-physical systems. In this paper, we consider safety specifications expressed in Signal Temporal Logic (STL). STL is a logic for specifying timed properties of real-...

We live in the information age, where many tasks are automated and many jobs are performed by machines. There has been a certain amount of fear about the rise of artificial intelligence (AI) and cognitive computing and its impact on traditional careers, ...

Advanced automation is needed in future mobile networks to provide adequate service quality economically and with high reliability. In this paper, a system is presented that takes into account the network context, analyses uncertain information, and ...