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08:30-09:00Coffee & Refreshments
09:00-10:00 Session 100F: Keynote
Location: Taub 4
Fallacious arguments: the place where Knowledge Representation and Argument Mining meet each other

ABSTRACT. Fallacies play a prominent role in argumentation since antiquity due to their contribution to argumentation in critical thinking education. They are defined as "derailments of strategic manoeuvring", meaning speech acts that violate the rules of a rational argumentative discussion for assumed persuasive gains. These derailments are particularly significant in political discourse, and the role of fallacies is becoming even more crucial nowadays as contemporary argumentation technologies face challenging tasks as misleading and manipulative information detection in news articles and political discourse, and counter-narrative generation. In this talk, I will discuss some solutions to identify automatically fallacious arguments in political debates, focusing on the prominent role of knowledge and reasoning in this challenging task.

10:00-10:30 Session 101: NMR and Learning
Location: Taub 4
There and Back Again: Combining Nonmonotonic Logical Reasoning and Deep Learning on an Assistive Robot
PRESENTER: Mohan Sridharan

ABSTRACT. This paper describes the development of an architecture that combines non-monotonic logical reasoning and deep learning in virtual/simulated and real/physical environments for a robot assisting in a restaurant environment. Specifically, for any given goal, the architecture uses Answer Set Prolog to represent and reason with incomplete commonsense domain knowledge, providing a sequence of actions for the robot to execute. At the same time, reasoning directs the robot's learning of deep neural network models in virtual environments for human face and hand gestures. These learned models are used by the robot to recognize and translate human gestures to goals that need to be achieved. We report insights learned from the development and evaluation of this architecture by a distributed team of researchers during the ongoing pandemic.

10:30-11:00Coffee Break
11:00-12:30 Session 102F: Argumentation 2


Location: Taub 4
Argumentation Frameworks induced by Assumption-based Argumentation: Relating Size and Complexity
PRESENTER: Markus Ulbricht

ABSTRACT. A key ingredient of computational argumentation in AI is the generation of arguments in favor or against claims under scrutiny. In this paper we look at the complexity of the argument generation procedure in the prominent structured formalism of assumption-based argumentation (ABA). We show several results connecting expressivity of ABA fragments and number of constructed arguments. First, for several NP-hard fragments of ABA, the number of generated arguments is not bounded polynomially. Even under equivalent rewritings of the given ABA framework there are situations where one cannot avoid an exponential blow-up. We establish a weaker notion of equivalence under which this blow-up can be avoided. As a general tool for analyzing ABA frameworks and resulting arguments and their conflicts, we extend results regarding dependency graphs of ABA frameworks, from which one can infer structural properties on the induced attacks among arguments.

Bipolar Argumentation Frameworks with Explicit Conclusions: Connecting Argumentation and Logic Programming
PRESENTER: Fabio Cozman

ABSTRACT. We introduce a formalism for bipolar argumentation frameworks that combines different proposals from the literature and results in a one-to-one correspondence with logic programming. We derive the correspondence by presenting translation algorithms from one formalism to the other and by evaluating the semantic equivalences between them. We also show that the bipolar model encapsulates distinct interpretations of the support relations studied the literature.

From Weighted Conditionals to a Gradual Argumentation Semantics and back

ABSTRACT. A fuzzy multipreference semantics has been recently proposed for weighted conditional knowledge bases with typicality, and used to develop a logical semantics for Multilayer Perceptrons, by regarding a deep neural network (after training) as a weighted conditional knowledge base. Based on different variants of this semantics, we propose some new gradual argumentation semantics, and relate them to the family of the gradual semantics. The paper also suggests an approach for defeasible reasoning over a weighted argumentation graph, building on the proposed semantics.

12:30-14:00Lunch Break

Lunches will be held in Taub lobby (CAV, CSF) and in The Grand Water Research Institute (DL, NMR, IJCAR, ITP).

14:00-15:30 Session 104F: Agents, actions and planning
Location: Taub 4
Modelling Agents Roles in the Epistemic Logic L-DINF

ABSTRACT. In this paper, we further advance a line of work aimed to formally model via epistemic logic (aspects of) the group dynamics of cooperative agents. In fact, we have previously proposed and here extend a particular logical framework (the Logic of ``Inferable'' L-DINF), where a group of cooperative agents can jointly perform actions. I.e., at least one agent of the group can perform the action, either with the approval of the group or on behalf of the group. So far, we have been able to take into consideration actions' \emph{cost} and the preferences that each agent can have for what concerns performing each action. In this paper, we introduce agents' \emph{roles} within a group. We choose to model roles in terms of the actions that each agent is enabled by its group to perform. We extend the semantics and the proof of strong completeness of our logic, and we show the usefulness of the new extension via a significant example.

A situation-calculus model of Knowledge and Belief based on Thinking about Justifications

ABSTRACT. This paper proposes an integration of the situation calculus with justification logic. Justification logic can be seen as a refinement of a modal logic of knowledge and belief to one in which knowledge not only is something that holds in all possible worlds, but also is justified. The work is an extension of that of Scherl and Levesque’s integration of the situation calculus with a modal logic of knowledge. We show that the solution developed here retains all of the desirable properties of the earlier solution while incorporating the enhanced expressibility of having justifications.

Towards Legally and Ethically Correct Online HTN Planning for Data Transfer
PRESENTER: Hisashi Hayashi

ABSTRACT. Data transfer among servers is crucial for distributed data mining because many databases are distributed around the world. However, as data privacy is becoming more legally and ethically protected, it is necessary to abide by the laws and respect the ethical guidelines when transferring and utilizing data. Because information affecting legal/ethical decision making is often distributed, the data-transfer plan must be updated online when new information is obtained while transferring data among servers. In this study, we propose a dynamic hierarchical task network (HTN) planning method that considers legal and ethical norms while planning multihop data transfers and data analyses/transformations. In our knowledge representation, we show that data-transfer tasks can be represented by the task-decomposition rules of total-order HTN planning, legal norms can be expressed as the preconditions of tasks and actions, and ethical norms can be expressed as the costs of tasks and actions. In the middle of the plan execution, the online planner dynamically updates the plan based on new information obtained in accordance with laws and ethical guidelines.

16:00-16:30Coffee Break
16:30-17:30 Session 108: Plenary
SMT-based Verification of Distributed Network Control Planes

ABSTRACT. The network control plane is a complex distributed system that runs various protocols for exchanging messages between routers and selecting paths for routing traffic. Errors in control plane configurations can lead to expensive outages or critical security breaches. The last decade has seen tremendous advances in applying formal methods to ensure their correctness.

In this talk, I will describe our logic-based approach that leverages Satisfiability Modulo Theory (SMT) solvers to verify a wide variety of network correctness properties including reachability, fault-tolerance, router equivalence, and load balancing. Although this approach is general and powerful, and works well for small-sized networks (with a few hundred routers), there are scalability challenges. I will then describe some recent improvements based on key abstractions and modular assume-guarantee reasoning that have enabled our SMT-based approach to successfully handle large-sized networks (with several thousands of routers), similar to those in operation in modern data centers.

This talk describes joint work with Ryan Beckett, Ratul Mahajan, Divya Raghunathan, Timothy Alberdingk Thijm, and David Walker.