Search Results for author: Timothy J. Norman

Found 20 papers, 6 papers with code

M-Arg: Multimodal Argument Mining Dataset for Political Debates with Audio and Transcripts

1 code implementation EMNLP (ArgMining) 2021 Rafael Mestre, Razvan Milicin, Stuart Middleton, Matt Ryan, Jiatong Zhu, Timothy J. Norman

In this paper, we present M-Arg, a multimodal argument mining dataset with a corpus of US 2020 presidential debates, annotated through crowd-sourced annotations.

Argument Mining

TAPE: Leveraging Agent Topology for Cooperative Multi-Agent Policy Gradient

1 code implementation25 Dec 2023 Xingzhou Lou, Junge Zhang, Timothy J. Norman, Kaiqi Huang, Yali Du

We propose Topology-based multi-Agent Policy gradiEnt (TAPE) for both stochastic and deterministic MAPG methods.

On Temporal References in Emergent Communication

no code implementations10 Oct 2023 Olaf Lipinski, Adam J. Sobey, Federico Cerutti, Timothy J. Norman

As humans, we use linguistic elements referencing time, such as before or tomorrow, to easily share past experiences and future predictions.

PRIME: A Price-Reverting Impact Model of a cryptocurrency Exchange

no code implementations12 May 2023 Christopher J. Cho, Timothy J. Norman, Manuel Nunes

In a financial exchange, market impact is a measure of the price change of an asset following a transaction.

Multi-trainer Interactive Reinforcement Learning System

no code implementations14 Oct 2022 Zhaori Guo, Timothy J. Norman, Enrico H. Gerding

In this paper, we propose a more effective interactive reinforcement learning system by introducing multiple trainers, namely Multi-Trainer Interactive Reinforcement Learning (MTIRL), which could aggregate the binary feedback from multiple non-perfect trainers into a more reliable reward for an agent training in a reward-sparse environment.

reinforcement-learning Reinforcement Learning (RL)

Optimising Long-Term Outcomes using Real-World Fluent Objectives: An Application to Football

no code implementations18 Feb 2021 Ryan Beal, Georgios Chalkiadakis, Timothy J. Norman, Sarvapali D. Ramchurn

In this paper, we present a novel approach for optimising long-term tactical and strategic decision-making in football (soccer) by encapsulating events in a league environment across a given time frame.

Decision Making

Combining Machine Learning and Human Experts to Predict Match Outcomes in Football: A Baseline Model

no code implementations8 Dec 2020 Ryan Beal, Stuart E. Middleton, Timothy J. Norman, Sarvapali D. Ramchurn

In this paper, we present a new application-focused benchmark dataset and results from a set of baseline Natural Language Processing and Machine Learning models for prediction of match outcomes for games of football (soccer).

BIG-bench Machine Learning

SHACL Satisfiability and Containment (Extended Paper)

no code implementations31 Aug 2020 Paolo Pareti, George Konstantinidis, Fabio Mogavero, Timothy J. Norman

We study the interaction of SHACL features in this logic and provide the detailed map of decidability and complexity results of the aforementioned decision problems for different SHACL sublanguages.


Optimising Game Tactics for Football

no code implementations23 Mar 2020 Ryan Beal, Georgios Chalkiadakis, Timothy J. Norman, Sarvapali D. Ramchurn

In this paper we present a novel approach to optimise tactical and strategic decision making in football (soccer).

Decision Making Game of Football

A Policy Editor for Semantic Sensor Networks

no code implementations15 Nov 2019 Paolo Pareti, George Konstantinidis, Timothy J. Norman

An important use of sensors and actuator networks is to comply with health and safety policies in hazardous environments.

SHACL Constraints with Inference Rules

1 code implementation1 Nov 2019 Paolo Pareti, George Konstantinidis, Timothy J. Norman, Murat Şensoy

On the one hand, SHACL constraints can be used to define a "schema" for graph datasets.

Rule Applicability on RDF Triplestore Schemas

1 code implementation2 Jul 2019 Paolo Pareti, George Konstantinidis, Timothy J. Norman, Murat Şensoy

Output schemas model the graphs that would be obtained by running the rules on the graph models of the input schema.

On Natural Language Generation of Formal Argumentation

no code implementations13 Jun 2017 Federico Cerutti, Alice Toniolo, Timothy J. Norman

In this paper we provide a first analysis of the research questions that arise when dealing with the problem of communicating pieces of formal argumentation through natural language interfaces.

Text Generation

Subjective Logic Operators in Trust Assessment: an Empirical Study

no code implementations19 Nov 2013 Federico Cerutti, Alice Toniolo, Nir Oren, Timothy J. Norman

Computational trust mechanisms aim to produce trust ratings from both direct and indirect information about agents' behaviour.

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