Spain

ECAI 2020

Rewrite or not Rewrite? ML-based Algorithm Selection for Datalog Query Answering on Knowledge Graphs

datalog query answering

algorithm selection

knowledge graphs

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

technical paper

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

Unsupervised Adversarial Learning of Anomaly Detection in the Wild

Ensemble Methods for Object Detection

Interpretable & Time-Budget-Constrained Contextualization for Re-Ranking

Learning to Predict Charges for Legal Judgment via Self-Attentive Capsule Network

Bidirectional Scene Text Recognition with a Single Decoder

Humans are capable of abstracting away irrelevant details or distinguish the common properties when studying problems. This is especially noticeable for planning problems, as humans are able to disregard certain parts of the domain and focus on the key elements important for finding a plan. Recently, the notion of abstraction has been introduced for Answer Set Programming (ASP), a knowledge representation and reasoning paradigm widely used in problem solving, with the potential to understand the key elements of a program that play a role in finding a solution. This paper highlights the potential use of such an abstraction in getting to the essence of planning problems, expressed in ASP, by disregarding irrelevant details and computing abstract plans.

Abstraction for ASP Planning

In some abstract argumentation framework (AAF), arguments have a degree of uncertainty, which impacts on the degree of uncertainty of the extensions obtained under a semantics. In these approaches, both the uncertainty of the arguments and of the extensions are modeled by means of precise probability values. However, in many real life situations the exact probabilities values are unknown and sometimes there is a need for aggregating the probability values of different sources. In this paper, we tackle the problem of calculating the degree of uncertainty of the extensions considering that the probability values of the arguments are imprecise.

Towards an Imprecise Probability Approach for Abstract Argumentation

Local to Global Learning of a Latent Dynamic Bayesian Network

From Eigentrust to a Trust-measuring algorithm in the Max-Plus Algebra

We describe a reasoning framework for knowledge invention and creative problem solving that can integrate and extend the knowledge level mechanism of diverse cognitive architectures (CAs). This framework exploits an extension of a Description Logic (DL) of typicality able to combine prototypical (commonsense) descriptions of concepts. It works as follows: given a goal expressed as a set of properties, in case an intelligent agent cannot find a concept in its knowledge base (KB) able to fulfill these properties, our framework is able to dynamically recombine, in a goal-oriented perspective, the concepts in the KB in order to find a suitable creative combination able to satisfy the goal. The KB of the agent is then extended via a mechanism of commonsense concept combination where the resulting combined concept represents the solution for the initial goal. Here we discuss how such framework is compliant with the general tenets of the Standard Model of Mind and can extend the knowledge level capabilities of diverse CAs.

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Unsupervised Adversarial Learning of Anomaly Detection in the Wild

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Epistemic Disjunctive Datalog for Querying Knowledge Bases

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Next from ECAI 2020

Unsupervised Adversarial Learning of Anomaly Detection in the Wild

Similar lecture

Epistemic Disjunctive Datalog for Querying Knowledge Bases

Downloads