I gave a chat within the workshop on how the synthesis of logic and equipment Discovering, In particular places like statistical relational Mastering, can allow interpretability.
Weighted product counting generally assumes that weights are only specified on literals, typically necessitating the need to introduce auxillary variables. We take into account a whole new strategy depending on psuedo-Boolean functions, bringing about a far more basic definition. Empirically, we also get SOTA outcomes.
The Lab carries out exploration in artificial intelligence, by unifying Discovering and logic, by using a latest emphasis on explainability
The paper discusses the epistemic formalisation of generalised setting up within the presence of noisy performing and sensing.
We think about the issue of how generalized programs (strategies with loops) might be deemed appropriate in unbounded and steady domains.
A consortia challenge on reputable systems and goverance was approved late last 12 months. News url here.
We have now a new paper acknowledged on Finding out exceptional linear programming objectives. We consider an “implicit“ hypothesis development tactic that yields nice theoretical bounds. Congrats to Gini and Alex on obtaining this paper acknowledged. Preprint below.
A journal paper has long been approved on prior constraints in tractable probabilistic types, obtainable around the papers tab. Congratulations Giannis!
Hyperlink In the final week of Oct, I gave a talk informally discussing explainability and moral accountability in artificial intelligence. Due to the organizers to the invitation.
Jonathan’s paper considers a lifted approached to weighted design integration, together with circuit construction. Paulius’ paper develops a measure-theoretic standpoint on weighted model counting and proposes a way to encode conditional weights on literals analogously to conditional probabilities, which ends up in sizeable functionality advancements.
Paulius' work on algorithmic methods for randomly producing logic applications and probabilistic logic applications is accepted towards the principles and practise of constraint programming (CP2020).
The framework is relevant to a big course of formalisms, which includes probabilistic relational models. The paper also reports the synthesis issue in that context. Preprint below.
For https://vaishakbelle.com/ anyone who is attending AAAI this calendar year, you may be interested in checking out our papers that touch on fairness, abstraction and generalized sum-solution troubles.
Our paper on synthesizing strategies with loops from the existence of probabilistic sounds, recognized the journal of approximate reasoning, has also been recognized into the ICAPS journal keep track of. Preprint to the entire paper in this article.