Federico Cerutti

Keywords: Introduction and motivation for the course Abstract Argumentation and Interfaces to Argumentative Reasoning

argumentation

Federico Cerutti

Keywords: Course Abstract Argumentation and Interfaces to Argumentative Reasoning: Abstract Argumentation Semantics Part 1

argumentation

Federico Cerutti

Keywords: Course Abstract Argumentation and Interfaces to Argumentative Reasoning: Abstract Argumentation Semantics Part 2

argumentation

Federico Cerutti

Keywords: Course Abstract Argumentation and Interfaces to Argumentative Reasoning: Abstract Argumentation Semantics Advanced Topics

argumentation

Federico Cerutti

Keywords: Course Abstract Argumentation and Interfaces to Argumentative Reasoning: Abstract Argumentation Semantics Advanced Topics

argumentation

Federico Cerutti

Keywords: Preliminary results on experiments with users of formal models of argumentation

argumentation

Federico Cerutti

Keywords: Example-driven illustration from knowledge base to abstract argumentation

argumentation

Federico Cerutti

Keywords: We enable aProbLog—a probabilistic logical programming approach—to reason in presence of uncertain probabilities represented as Beta-distributed random variables. We achieve the same performance of state-of-the-art algorithms for highly specified and engineered domains, while simultaneously we maintain the flexibility offered by aProbLog in handling complex relational domains. Our motivation is that faithfully capturing the distribution of probabilities is necessary to compute an expected utility for effective decision making under uncertainty: unfortunately, these probability distributions can be highly uncertain due to sparse data. To understand and accurately manipulate such probability distributions we need a well-defined theoretical framework that is provided by the Beta distribution, which specifies a distribution of probabilities representing all the possible values of a probability when the exact value is unknown.

probabilistic logic programming; uncertain probabilities

Federico Cerutti

Keywords: In this paper we provide a critical analysis with met- rics that will inform guidelines for designing distributed systems for Collective Situational Understanding (CSU). CSU requires both collective insight—i.e., accurate and deep understanding of a situation derived from uncertain and often sparse data and collective foresight—i.e., the ability to predict what will happen in the future. When it comes to complex scenarios, the need for a distributed CSU naturally emerges, as a single monolithic approach not only is unfeasible: it is also undesirable. We therefore propose a principled, critical analysis of AI techniques that can support specific tasks for CSU to derive guidelines for designing distributed systems for CSU.

human-machine knowledge fusion

Federico Cerutti

Keywords: In this paper we propose a computational method- ology for assessing the impact of trust associated to sources of information in scientific enquiry activities building upon recent proposals of an ontology for situational understanding and results in computational argumentation. Often trust in the source of information serves as a proxy for evaluating the quality of the information itself, especially in the cases of information overhead. We show how our computational methodology, composed of an ontology for representing uncertain information and sources, as well as an argumentative process of conjecture and refutation, support human analysts in scientific enquiry, as well as high- lighting issues that demand further investigation.

argumentation; scientific enquiry; intelligence analysis

Federico Cerutti

Keywords: Tutorial on argumentation with a focus on SAT-based algorithms

argumentation; algorithms

Barbara J. Grosz

Keywords: This talk will describe Harvard’s Embedded EthiCS program, a novel approach to integrating ethics into computer science education that incorporates ethical reasoning throughout courses in the standard computer science curriculum. The talk will describe the goals of Embedded EthiCS, the way the program works, lessons learned and challenges to sustainable implementations of such a program across different types of academic institutions.

Digital Humanism; AI; Teaching

Stuart Russell

Keywords: How can we harness the power of superintelligent AI while also preventing the catastrophe of robotic takeover? As we move closer toward creating all-knowing machines, AI pioneer Stuart Russell is working on something a bit different: robots with uncertainty. Hear his vision for human-compatible AI that can solve problems using common sense, altruism and other human values.

Human-compatible AI; Digital Humanism

IntelligenceSquared Debates

Keywords: In partnership with IBM, Intelligence Squared U.S. is hosting a unique debate between a world-class champion debater and an AI system. IBM Project Debater is the first AI system designed to debate humans on complex topics using a combination of pioneering research developed by IBM researchers, including: data-driven speechwriting and delivery, listening comprehension, and modeling human dilemmas.

AI; Debates

Andreas Holzinge

Keywords: Due to raising ethical, social and legal issues, future AI supported systems must be made transparent, re-traceable, thus human interpretable. Andreas’ aim is to explain why a machine decision has been reached, paving the way towards explainable AI and Causability, ultimately fostering ethical responsible machine learning.

Explainable AI; Human-centered AI

Professor Chris Reed

Keywords: Professor Chris Reed from the ARG-tech group at the University of Dundee gives an overview of the BBC project involving ARG-tech and the Radio 4 Moral Maze programme as well as the live BBC 2 debate, Abortion on Trial. He also talks about how the relationship with the BBC came about.

Argumentation; Moral Maze

Matthias Thimm and Tjitze Rienstra

Keywords: We present approximation algorithms for reasoning in structured argumentation approaches such as ASPIC+.

argumentation; algorithms

Didier Dubois and Henri Prade

Keywords: Possibility theory; Machine learning

How possiblity theory could be used as the basis of machine learning of rules

Isabelle Kuhlmann and Matthias Thimm

Keywords: inconsistency measurement; algorithms

We present an algorithm for determining inconsistency degrees wrt. the contension inconsistency measure based on answer set programming.

Xiaoting Shao, Arseny Skryagin, Zhongjie Yu, Tjitze Rienstra, Matthias Thimm and Kristian Kersting

Keywords: ranking functions; probabilistic reasoning

We introduce min-sum networks (MSNs) for a compact representation of ranking functions for multiple variables.

Matthias Thimm

Keywords: description logic