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- research-articleSeptember 2024
Incremental measurement of structural entropy for dynamic graphs
AbstractStructural entropy is a metric that measures the amount of information embedded in graph structure data under a strategy of hierarchical abstracting. To measure the structural entropy of a dynamic graph, we need to decode the optimal encoding ...
- research-articleSeptember 2024
An extensive study of security games with strategic informants
AbstractOver the past years, game-theoretic modeling for security and public safety issues (also known as security games) have attracted intensive research attention and have been successfully deployed in many real-world applications for fighting, e.g., ...
- research-articleSeptember 2024
Functional Relation Field: A Model-Agnostic Framework for Multivariate Time Series Forecasting
AbstractIn multivariate time series forecasting, the most popular strategy for modeling the relationship between multiple time series is the construction of graph, where each time series is represented as a node and related nodes are connected by edges. ...
Highlights- Explicitly and precisely characterize the underlying constraints between multiple time series.
- Flexible and model-agnostic framework as a new inductive bias for multi-variate time series.
- Consistently improve the prediction ...
- research-articleSeptember 2024
Probabilistic reach-avoid for Bayesian neural networks
AbstractModel-based reinforcement learning seeks to simultaneously learn the dynamics of an unknown stochastic environment and synthesise an optimal policy for acting in it. Ensuring the safety and robustness of sequential decisions made through a policy ...
- research-articleDecember 2023
Balanced Q-learning: Combining the influence of optimistic and pessimistic targets
- Thommen George Karimpanal,
- Hung Le,
- Majid Abdolshah,
- Santu Rana,
- Sunil Gupta,
- Truyen Tran,
- Svetha Venkatesh
AbstractThe optimistic nature of the Q−learning target leads to an overestimation bias, which is an inherent problem associated with standard Q−learning. Such a bias fails to account for the possibility of low returns, particularly in risky scenarios. ...
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- research-articleDecember 2023
Robust vehicle lane keeping control with networked proactive adaptation
AbstractRoad condition is an important environmental factor for autonomous vehicle control. A dramatic change in the road condition from the nominal status is a source of uncertainty that can lead to a system failure. Once the vehicle encounters an ...
- research-articleDecember 2023
Risk-averse receding horizon motion planning for obstacle avoidance using coherent risk measures
AbstractThis paper studies the problem of risk-averse receding horizon motion planning for agents with uncertain dynamics, in the presence of stochastic, dynamic obstacles. We propose a model predictive control (MPC) scheme that formulates the obstacle ...
- research-articleDecember 2023
Mathematical runtime analysis for the non-dominated sorting genetic algorithm II (NSGA-II)
AbstractThe non-dominated sorting genetic algorithm II (NSGA-II) is the most intensively used multi-objective evolutionary algorithm (MOEA) in real-world applications. However, in contrast to several simple MOEAs analyzed also via mathematical means, no ...
- research-articleDecember 2023
Computing optimal hypertree decompositions with SAT
AbstractHypertree width is a prominent hypergraph invariant with many algorithmic applications in constraint satisfaction and databases. We propose two novel characterisations for hypertree width in terms of linear orderings. We utilize these ...
- research-articleDecember 2023
A k-additive Choquet integral-based approach to approximate the SHAP values for local interpretability in machine learning
AbstractBesides accuracy, recent studies on machine learning models have been addressing the question on how the obtained results can be interpreted. Indeed, while complex machine learning models are able to provide very good results in terms of accuracy ...
- research-articleNovember 2023
Lifted inference with tree axioms
AbstractWe consider the problem of weighted first-order model counting (WFOMC): given a first-order sentence ϕ and domain size n ∈ N, determine the weighted sum of models of ϕ over the domain { 1 , … , n }. Past work has shown that any sentence using at ...
- research-articleNovember 2023
A Bayesian approach to (online) transfer learning: Theory and algorithms
AbstractTransfer learning is a machine learning paradigm where knowledge from one problem is utilized to solve a new but related problem. While conceivable that knowledge from one task could help solve a related task, if not executed properly, transfer ...
- research-articleNovember 2023
Risk-aware shielding of Partially Observable Monte Carlo Planning policies
AbstractPartially Observable Monte Carlo Planning (POMCP) is a powerful online algorithm that can generate approximate policies for large Partially Observable Markov Decision Processes. The online nature of this method supports scalability by avoiding ...
- research-articleMay 2023
Axiomatic characterization of PageRank
AbstractThis paper examines the fundamental problem of identifying the most important nodes in a network. To date, more than a hundred centrality measures have been proposed, each evaluating the position of a node in a network from a different ...
- research-articleMay 2023
On approximating shortest paths in weighted triangular tessellations
AbstractWe study the quality of weighted shortest paths when a continuous 2-dimensional space is discretized by a weighted triangular tessellation. In order to evaluate how well the tessellation approximates the 2-dimensional space, we study three types ...
- research-articleMay 2023
Temporal logic explanations for dynamic decision systems using anchors and Monte Carlo Tree Search
AbstractFor many automated perception and decision tasks, state-of-the-art performance may be obtained by algorithms that are too complex for their behavior to be completely understandable or predictable by human users, e.g., because they employ large ...
- research-articleMay 2023
Better bounds on the adaptivity gap of influence maximization under full-adoption feedback
AbstractIn the influence maximization (IM) problem, we are given a social network and a budget k, and we look for a set of k nodes in the network, called seeds, that maximize the expected number of nodes that are reached by an influence cascade generated ...
- research-articleMay 2023
Expanding the prediction capacity in long sequence time-series forecasting
AbstractMany real-world applications show growing demand for the prediction of long sequence time-series, such as electricity consumption planning. Long sequence time-series forecasting (LSTF) requires a higher prediction capacity of the model, which is ...
- research-articleMarch 2023
Accurate parameter estimation for safety-critical systems with unmodeled dynamics
AbstractAnalysis and synthesis of safety-critical autonomous systems are carried out using models which are often dynamic. Two central features of these dynamic systems are parameters and unmodeled dynamics. Much of feedback control design is parametric ...
- research-articleMarch 2023
GUBS criterion: Arbitrary trade-offs between cost and probability-to-goal in stochastic planning based on Expected Utility Theory
AbstractStochastic Shortest Path MDPs (SSP-MDPs) are used to model probabilistic sequential decision problems where the objective is to minimize the expected accumulated cost to goal. However, in the presence of dead-ends, the conventional criterion for ...