Mario Brčič

Publications38
h-index7
Citations489
Highly Influential Citations24
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Explainable artificial intelligence: A survey
TLDR
Recent developments in XAI in supervised learning are summarized, a discussion on its connection with artificial general intelligence is started, and proposals for further research directions are given.
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Resource Constrained Project Scheduling under Uncertainty: A Survey
TLDR
The survey of methods and models that are put into the historical context and are categorized according to their working principles are presented, which aims to supplement and update existing RCPSP surveys.
Planning horizons based proactive rescheduling for stochastic resource-constrained project scheduling problems
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Impossibility Results in AI: A Survey
TLDR
This paper categorized impossibility theorems applicable to the domain of AI into five categories: deduction, indistinguishability, induction, tradeoffs, and intractability, and concluded that deductive impossibilities deny 100%-guarantees for security.
View PDF on arXiv
Tracking Predictive Gantt Chart for Proactive Rescheduling in Stochastic Resource Constrained Project Scheduling
TLDR
It is demonstrated that in the state-of-the-art proactive-reactive scheduling, the baseline schedule is agnostic to the information received during the project execution, and the sources of such inflexibility in the problem model and the scheduling methods are analyzed.
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Towards intelligent compiler optimization
The future of computation is massively parallel and heterogeneous with specialized accelerator devices and instruction sets in both edge- and cluster-computing. However, software development is bound…
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Combinatorial testing in software projects
TLDR
The paper presents a survey of research into combinatorial testing suite factors while also identifying possible future research ideas into experimental design extensions for software testing.
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Explainability in reinforcement learning: perspective and position
TLDR
This position paper attempts to give a systematic overview of existing methods in the explainable RL area and propose a novel unified taxonomy, building and expanding on the existing ones.
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Exact solving scheduling problems accelerated by graph neural networks
Scheduling is a family of combinatorial problems where we need to find optimal time arrangements for activities. Scheduling problems in applications are usually notoriously hard to solve exactly.…
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Explainable Artificial Intelligence: An Updated Perspective
Artificial intelligence has become mainstream and its applications will only proliferate. Specific measures must be done to integrate such systems into society for the general benefit. One of the…
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