SciTePress - Affiliation Profile

Affiliation

Select a Subject
Refine Ontology Subject

University of Bristol, United Kingdom

Context (please use complete words only)

Field

Sort by

45 Papers

The Devil Lies in the Details for Species Coexistence Stability in Ablated and Unablated Five-Species Evolutionary Spatial Cyclic Games

Topics: Biostatistics and Stochastic Models; Model Design and Evaluation; Simulation and Modeling

Author: Dave Cliff

Affiliation: School of Engineering Mathematics and Technology, University of Bristol, Bristol BS8 1UB, U.K.

Digital Object Identifier: 10.5220/0013132500003911

Proceedings of BIOINFORMATICS 2025, ISBN: 978-989-758-731-3

Quick Abstract

I present exploration of key results from the series of ablated five-species “Rock-Paper-Scissors-Lizard-Spock” minimal agent-based evolutionary models of biodiversity introduced by Zhong, Zhang, Li, Dai, & Yang in their 2022 paper “Species coexistence in spatial cyclic game of five species” (Chaos, Solitons and Fractals, 156: 111806). At the heart of Zhong et al.’s model of ecosystems coexistence is the Elementary Step (ES) algorithm in which one or two neighboring agents are chosen at random to engage in one or more interactions selected at random from the set {COMPETE, REPRODUCE, MOVE}. Minor revisions to the ES algorithm have recently been introduced to make it more computationally efficient in space and in time, and one contribution of this paper is to demonstrate that switching to this “Revised ES” (RES) has the unexpected effect of totally changing the outcomes of Zhong et al.’s simulation experiments. I present analysis of the RES-based experiments which shows that the key difference is that in RES the likelihood of an agent moving is decoupled from the likelihood of an agent reproducing or competing, whereas in the original ES the likelihoods of the three possible actions are interdependently coupled. The fact that such relatively minor changes to the ES algorithm result in such major changes in the experiment outcomes casts significant doubt on the extent to which results such as those from Zhong et al.’s original experiments can be trusted as truly representative of the real-world biological systems that they are supposedly intended to model. Python source-code available on GitHub can be used to replicate the results presented here.

Studying Narrative Economics by Adding Continuous-Time Opinion Dynamics to an Agent-Based Model of Co-Evolutionary Adaptive Financial Markets

Topics: Multi-Agent Systems; Evolutionary Computing; Agent Models and Architectures; Simulation; Economic Agent Models; Intelligent Auctions and Markets

Authors: Arwa Bokhari and Dave Cliff

Affiliation: Department of Computer Science, University of Bristol, Bristol BS8 1UB, U.K.

Digital Object Identifier: 10.5220/0011797000003393

Proceedings of ICAART 2023, ISBN: 978-989-758-623-1

Quick Abstract

In 2017 Robert Shiller, a Nobel Laureate, introduced Narrative Economics, an approach to explaining aspects of economies that are difficult to comprehend when analyzed using conventional methods: in light of narratives (i.e., stories) that participants in asset markets hear, believe, and tell each other, some observable economic factors, such as price dynamics of otherwise valueless digital assets, can be explained largely within the context of those narratives. As Shiller argues, it is best to explain and understand seemingly irrational and hard-to-explain behaviors, such as investing in highly volatile cryptocurrency markets, in narrative terms: people invest because they believe that it makes sense to do so, or have a heartfelt opinion about the prospects of the asset, and they share these beliefs and opinions with themselves and others in the form of narratives. In this paper, we address the question of how an agent-based modeling platform can be developed to be used for studying narrative economics. To do this, we integrate two very recently published developments. From the field of agent-based models of financial markets, we use the PRDE adaptive zero-intelligence trader strategy introduced by Cliff (2022), and we extend it to integrate a continuous-time real-valued nonlinear opinion dynamics model reported by Bizyaeva et al. (2022). In our integrated system, each trader holds an opinion variable whose value can be altered by interaction with other agents, modeling the influence that narratives have on an agent’s opinions, and which can also be altered by observation of events in the market. Furthermore, the PRDE algorithm is modified to allow each trader’s trading behavior to smoothly alter as that trader’s opinion dynamically varies. Results reported for the first time here show that in our model there is a tightly coupled circular interplay between opinions and prices: changes in the distribution of opinions can affect subsequent price dynamics; and changes in price dynamics can affect the consequent distribution of opinions. Thus this paper presents a first demonstration of the reliability and effectiveness of our new agent-based modeling platform for use in studying issues in narrative economics. Python source-code for our model is being made freely available as open-source release on GitHub, to allow other researchers to replicate and extend our work.

Triple-stream Deep Metric Learning of Great Ape Behavioural Actions

Topic: Deep Learning for Visual Understanding

Authors: Otto Brookes ¹ , Majid Mirmehdi ¹ , Hjalmar Kühl ² and Tilo Burghardt ¹

Affiliations: ¹ Department of Computer Science, University of Bristol, U.K. , ² Evolutionary and Anthropocene Ecology, iDiv, Leipzig, Germany

Digital Object Identifier: 10.5220/0011798400003417

Proceedings of VISAPP 2023/VISIGRAPP, ISBN: 978-989-758-634-7

Quick Abstract

We propose the first metric learning system for the recognition of great ape behavioural actions. Our proposed triple stream embedding architecture works on camera trap videos taken directly in the wild and demonstrates that the utilisation of an explicit DensePose-Chimp body part segmentation stream effectively complements traditional RGB appearance and optical flow streams. We evaluate system variants with different feature fusion techniques and long-tail recognition approaches. Results and ablations show performance improvements of ~12% in top-1 accuracy over previous results achieved on the PanAf-500 dataset containing 180,000 manually annotated frames across nine behavioural actions. Furthermore, we provide a qualitative analysis of our findings and augment the metric learning system with long-tail recognition techniques showing that average per class accuracy -- critical in the domain -- can be improved by ~23% compared to the literature on that dataset. Finally, since our embedding spaces are constructed as metric, we provide first data-driven visualisations of the great ape behavioural action spaces revealing emerging geometry and topology. We hope that the work sparks further interest in this vital application area of computer vision for the benefit of endangered great apes. We provide all key source code and network weights alongside this publication.

Narrative Economics of the Racetrack: An Agent-Based Model of Opinion Dynamics in In-play Betting on a Sports Betting Exchange

Topics: Multi-Agent Systems; Simulation; Economic Agent Models; Intelligent Auctions and Markets

Authors: Rasa Guzelyte and Dave Cliff

Affiliation: Department of Computer Science, University of Bristol, Bristol BS8 1UB, U.K.

Digital Object Identifier: 10.5220/0010834800003116

Proceedings of ICAART 2022, ISBN: 978-989-758-547-0

Quick Abstract

We present first results from a new agent-based model (ABM) of a sports-betting exchange (such as those operated by BetFair, BetDdaq, and SMarkets, among other companies) in which each agent holds a dynamically- varying opinion about some uncertain future event (such as which competitor will win a particular horse race) and in which all agents interact with the betting exchange to find counterparties holding an opposing view with whom they can then enter into a bet with. We extend methods from Opinion Dynamics (OD) research to give each agent an opinion at any particular time which is influenced partially by local interactions with other agents (as is common in the OD literature), partially by globally available information (as published to all by the betting exchange) and partially by the progressive reduction in uncertainty in the system (i.e., eventually all agents know which horse has won the race). Our work here is motivated by the prize-winning ICAART2021 paper of Lomas & Cliff, who integrated OD methods with ABMs of financial markets to explore issues in Narrative Economics, an approach recently proposed and popularised by Nobel Laureate Robert Shiller, but here we explore a significantly different type of market: a betting market (which has strong similarities to a financial market for tradeable derivative contracts such as futures or options). The novel contributions of this paper are centred on the extension of OD methods to situations in which there is a mix of local and global influence, and in which uncertainty progressively reduces to zero. We present results from our initial proof-of-concept implementation. The Python source-code for our ABM is freely available on Github for other researchers to replicate and extend the work reported here.

TVNet: Temporal Voting Network for Action Localization

Topics: Event and Human Activity Recognition; Deep Learning for Visual Understanding

Authors: Hanyuan Wang , Dima Damen , Majid Mirmehdi and Toby Perrett

Affiliation: Department of Computer Science, University of Bristol, Bristol, U.K.

Digital Object Identifier: 10.5220/0010868900003124

Proceedings of VISAPP 2022/VISIGRAPP, ISBN: 978-989-758-555-5

Quick Abstract

We propose a Temporal Voting Network (TVNet) for action localization in untrimmed videos. This incorporates a novel Voting Evidence Module to locate temporal boundaries, more accurately, where temporal contextual evidence is accumulated to predict frame-level probabilities of start and end action boundaries. Our action-independent evidence module is incorporated within a pipeline to calculate confidence scores and action classes. We achieve an average mAP of 34.6% on ActivityNet-1.3, particularly outperforming previous methods with the highest IoU of 0.95. TVNet also achieves mAP of 56.0% when combined with PGCN and 59.1% with MUSES at 0.5 IoU on THUMOS14 and outperforms prior work at all thresholds. Our code is available at https://github.com/hanielwang/TVNet.

Exploring Narrative Economics: An Agent-based-modeling Platform that Integrates Automated Traders with Opinion Dynamics

Topics: Multi-Agent Systems; Economic Agent Models; Intelligent Auctions and Markets

Authors: Kenneth Lomas and Dave Cliff

Affiliation: Department of Computer Science, University of Bristol, Bristol BS8 1UB, U.K.

Digital Object Identifier: 10.5220/0010337101370148

Proceedings of ICAART 2021, ISBN: 978-989-758-484-8

Quick Abstract

In seeking to explain aspects of real-world economies that defy easy understanding when analysed via conventional means, Nobel Laureate Robert Shiller has since 2017 introduced and developed the idea of Narrative Economics, where observable economic factors such as the dynamics of prices in asset markets are explained largely as a consequence of the narratives (i.e., the stories) heard, told, and believed by participants in those markets. Shiller argues that otherwise irrational and difficult-to-explain behaviors, such as investors participating in highly volatile cryptocurrency markets, are best explained and understood in narrative terms: people invest because they believe, because they have a heartfelt opinion, about the future prospects of the asset, and they tell to themselves and others stories (narratives) about those beliefs and opinions. In this paper we describe what is, to the best of our knowledge, the first ever agent-based modelling platform that allows for the study of issues in narrative economics. We have created this by integrating and synthesizing research in two previously separate fields: opinion dynamics (OD), and agent-based computational economics (ACE) in the form of minimally-intelligent trader-agents operating in accurately modelled financial markets. We show here for the first time how long-established models in OD and in ACE can be brought together to enable the experimental study of issues in narrative economics, and we present initial results from our system. The program-code for our simulation platform has been released as freely-available open-source software on GitHub, to enable other researchers to replicate and extend our work.

Market Impact in Trader-agents: Adding Multi-level Order-flow Imbalance-sensitivity to Automated Trading Systems

Topics: Multi-Agent Systems; Industrial Applications of AI; Economic Agent Models; Intelligent Auctions and Markets

Authors: Zhen Zhang and Dave Cliff

Affiliation: Department of Computer Science, University of Bristol, Bristol BS8 1UB, U.K.

Digital Object Identifier: 10.5220/0010391004260436

Proceedings of ICAART 2021, ISBN: 978-989-758-484-8

Quick Abstract

Financial markets populated by human traders often exhibit so-called “market impact”, where the prices quoted by traders move in the direction of anticipated change, before any transaction has taken place, as an immediate reaction to the arrival of a large (i.e., “block”) buy or sell order in the market: traders in the market know that a block buy order is likely to push the price up, and that a block sell order is likely to push the price down, and so they immediately adjust their quote-prices accordingly. In most major financial markets nowadays very many of the participants are “robot traders”, autonomous adaptive software agents, rather than humans. This paper addresses the question of how to give such trader-agents a reliable anticipatory sensitivity to block orders, such that markets populated entirely by robot traders also show market-impact effects. This is desirable because impact-sensitive trader-agents will get a better price for their transactions when block orders arrive, and because such traders can also be used for more accurate simulation models of real-world financial markets. In a 2019 publication Church & Cliff presented initial results from a simple deterministic robot trader, called ISHV, which was the first such trader-agent to exhibit this market impact effect. ISHV does this via monitoring a metric of imbalance between supply and demand in the market. The novel contributions of our paper are: (a) we critique the methods used by Church & Cliff, revealing them to be weak, and argue that a more robust measure of imbalance is required; (b) we argue for the use of multi-level order-flow imbalance (MLOFI: Xu et al., 2019) as a better basis for imbalance-sensitive robot trader-agents; and (c) we demonstrate the use of the more robust MLOFI measure in extending ISHV, and also the well-known AA and ZIP trading-agent algorithms (which have both been previously shown to consistently outperform human traders). Our results demonstrate that the new imbalance-sensitive trader-agents introduced in this paper do exhibit market impact effects, and hence are better-suited to operating in markets where impact is a factor of concern or interest, but do not suffer the weaknesses of the methods used by Church & Cliff. We have made the source-code for our work reported here freely available on GitHub.

No Need for a Lab: Towards Multi-sensory Fusion for Ambient Assisted Living in Real-world Living Homes

Topics: Event and Human Activity Recognition; Deep Learning for Visual Understanding ; Assistive Computer Vision

Authors: Alessandro Masullo , Toby Perrett , Dima Damen , Tilo Burghardt and Majid Mirmehdi

Affiliation: University of Bristol, BS8 1UB, Bristol, U.K.

Digital Object Identifier: 10.5220/0010202903280337

Proceedings of VISAPP 2021/VISIGRAPP, ISBN: 978-989-758-488-6

Quick Abstract

The majority of the Ambient Assisted Living (AAL) systems, designed for home or lab settings, monitor one participant at a time – this is to avoid the complexities of pre-fusion correspondence of different sensors since carers, guests, and visitors may be involved in real world scenarios. Previous work from (Masullo et al., 2020) presented a solution to this problem that involves matching video sequences of silhouettes to accelerations from wearable sensors to identify members of a household while respecting their privacy. In this work, we elevate this approach to the next stage by improving its architecture and combining it with a tracking functionality that makes it possible to be deployed in real-world homes. We present experiments on a new dataset recorded in participants’ own houses, which includes multiple participants visited by guests, and show an auROC score of 90.2%. We also show a novel first example of subject-tailored health monitoring measurement by applying our methodology to a sit-to-stand detector to generate clinically relevant rehabilitation trends.

A Fine-grained Perspective onto Object Interactions from First-person Views

Author: Dima Damen

Affiliation: University of Bristol, United Kingdom

Digital Object Identifier: 10.5220/0008345900110013

Proceedings of VISIGRAPP 2019, ISBN: 978-989-758-354-4

Quick Abstract

Traditionally, action understanding has been limited to assigning one out of a pre-selected set of labels to a trimmed video sequence. This talk goes beyond traditional action recognition to fine-grained understand of daily object interactions. The talk will discuss works that attempt to understand ‘when’ an object interaction takes place, including ‘when’ it can be considered completed, ‘which’ semantic labels can describe the interactions, ‘how’ the interaction can be described (or captioned) and ‘who’ is better when contrasting people perform the same interaction. Potentials and limitations of current deep architectures to solve for fine-grained object interaction understanding will be discussed. The talk will focus on the first-person viewpoint –captured using wearable cameras- as it offers a unique perspective onto objects during interactions.

Exhaustive Testing of Trader-agents in Realistically Dynamic Continuous Double Auction Markets: AA Does Not Dominate

Topics: Multi-Agent Systems; Industrial Applications of AI; Agent Models and Architectures; Simulation; Economic Agent Models; Intelligent Auctions and Markets

Author: Dave Cliff

Affiliation: Department of Computer Science University of Bristol, Bristol BS8 1UB and U.K

Digital Object Identifier: 10.5220/0007382802240236

Proceedings of ICAART 2019, ISBN: 978-989-758-350-6

Quick Abstract

We analyse results from over 3.4million detailed market-trading simulation sessions which collectively confirm an unexpected result: in markets with dynamically varying supply and demand, the best-performing automated adaptive auction-market trading-agent currently known in the AI/Agents literature, i.e. Vytelingum’s Adaptive-Aggressive (AA) strategy, can be routinely out-performed by simpler trading strategies. AA is the most recent in a series of AI trading-agent strategies proposed by various researchers over the past twenty years: research papers contributing major steps in this evolution of strategies have been published at IJCAI, in the Artificial Intelligence journal, and at AAMAS. The innovative step taken here is to brute-force exhaustively evaluate AA in market environments that are in various ways more realistic, closer to real-world financial markets, than the simple constrained abstract experimental evaluations routinely used in the prior academic AI/Agents research literature. We conclude that AA can indeed appear dominant when tested only against other AI-based trading agents in the highly simplified market scenarios that have become the methodological norm in the trading-agents academic research literature, but much of that success seems to be because AA was designed with exactly those simplified experimental markets in mind. As soon as we put AA in scenarios closer to real-world markets, modify it to fit those markets accordingly, and exhaustively test it against simpler trading agents, AA’s dominance simply disappears.

Related Ontology Subjects/Areas/Topics (20)

Agent Models and Architectures	Economic Agent Models	Multi-Agent Systems
Agents	Enterprise Information Systems	Operational Research
Artificial Intelligence	Industrial Applications of AI	Simulation
Artificial Intelligence and Decision Support Systems	Information Systems Analysis and Specification	Soft Computing
Bioinformatics	Knowledge Engineering and Ontology Development	Software Engineering
Biomedical Engineering	Knowledge-Based Systems	Symbolic Systems
Distributed and Mobile Software Systems	Methodologies and Technologies

Topic(s):

Filter listed results, by

Publication date

From:

To:

Author

Locate Author in list below

Data pager
Data pager
1 2 3 4 5 6 7 8 Page 1 of 8, items 1 to 10 of 72.

Add Author(s) to filter:

Dave Cliff (14)

Majid Mirmehdi (8)

John Cartlidge (5)

Neill Campbell (4)

Marco De Luca (3)

Andrew Calway (3)

Siyuan Fang (3)

Nello Cristianini (3)

Sion Hannuna (3)

Owen Rogers (3)

Conference

Locate Conference in list below

Data pager
Data pager
1 2 Page 1 of 2, items 1 to 10 of 12.

Add Conference(s) to filter:

ICAART(13)

VISAPP(8)

ICPRAM(8)

GRAPP(5)

CLOSER(2)

BIODEVICES(2)

ICINCO(2)

BIOINFORMATICS(1)

CSEDU(1)

NCTA(1)

Title:
ISBN:
Year:
Acronym:
Subject:

Title:
Author:
Affiliation:
Subject:

Name:
Affiliation:
Country:
Conference:
Subject:

Name:
Country:
Subject: