8,054 research outputs found
Agent-based Modeling And Market Microstructure
In most modern financial markets, traders express their preferences for assets by making orders. These orders are either executed if a counterparty is willing to match them or collected in a priority queue, called a limit order book. Such markets are said to adopt an order-driven trading mechanism. A key question in this domain is to model and analyze the strategic behavior of market participants, in response to different definitions of the trading mechanism (e.g., the priority queue changed from the continuous double auctions to the frequent call market). The objective is to design financial markets where pernicious behavior is minimized.The complex dynamics of market activities are typically studied via agent-based modeling (ABM) methods, enriched by Empirical Game-Theoretic Analysis (EGTA) to compute equilibria amongst market players and highlight the market behavior (also known as market microstructure) at equilibrium. This thesis contributes to this research area by evaluating the robustness of this approach and providing results to compare existing trading mechanisms and propose more advanced designs.In Chapter 4, we investigate the equilibrium strategy profiles, including their induced market performance, and their robustness to different simulation parameters. For two mainstream trading mechanisms, continuous double auctions (CDAs) and frequent call markets (FCMs), we find that EGTA is needed for solving the game as pure strategies are not a good approximation of the equilibrium. Moreover, EGTA gives generally sound and robust solutions regarding different market and model setups, with the notable exception of agents’ risk attitudes. We also consider heterogeneous EGTA, a more realistic generalization of EGTA whereby traders can modify their strategies during the simulation, and show that fixed strategies lead to sufficiently good analyses, especially taking the computation cost into consideration.After verifying the reliability of the ABM and EGTA methods, we follow this research methodology to study the impact of two widely adopted and potentially malicious trading strategies: spoofing and submission of iceberg orders. In Chapter 5, we study the effects of spoofing attacks on CDA and FCM markets. We let one spoofer (agent playing the spoofing strategy) play with other strategic agents and demonstrate that while spoofing may be profitable in both market models, it has less impact on FCMs than on CDAs. We also explore several FCM mechanism designs to help curb this type of market manipulation even further. In Chapter 6, we study the impact of iceberg orders on the price and order flow dynamics in financial markets. We find that the volume of submitted orders significantly affects the strategy choice of the other agents and the market performance. In general, when agents observe a large volume order, they tend to speculate instead of providing liquidity. In terms of market performance, both efficiency and liquidity will be harmed. We show that while playing the iceberg-order strategy can alleviate the problem caused by the high-volume orders, submitting a large enough order and attracting speculators is better than taking the risk of having fewer trades executed with iceberg orders.We conclude from Chapters 5 and 6 that FCMs have some exciting features when compared with CDAs and focus on the design of trading mechanisms in Chapter 7. We verify that CDAs constitute fertile soil for predatory behavior and toxic order flows and that FCMs address the latency arbitrage opportunities built in those markets. This chapter studies the extent to which adaptive rules to define the length of the clearing intervals — that might move in sync with the market fundamentals — affect the performance of frequent call markets. We show that matching orders in accordance with these rules can increase efficiency and selfish traders’ surplus in a variety of market conditions. In so doing, our work paves the way for a deeper understanding of the flexibility granted by adaptive call markets
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Computational Argumentation-based Chatbots: a Survey
The article archived on this institutional repository is a preprint. It has not been certified by peer review.Chatbots are conversational software applications designed to interact dialectically with users for a plethora of different purposes. Surprisingly, these colloquial agents have only recently been coupled with computational models of arguments (i.e. computational argumentation), whose aim is to formalise, in a machine-readable format, the ordinary exchange of information that characterises human communications. Chatbots may employ argumentation with different degrees and in a variety of manners. The present survey sifts through the literature to review papers concerning this kind of argumentation-based bot, drawing conclusions about the benefits and drawbacks that this approach entails in comparison with standard chatbots, while also envisaging possible future development and integration with the Transformer-based architecture and state-of-the-art Large Language models
Right Place, Right Time:Proactive Multi-Robot Task Allocation Under Spatiotemporal Uncertainty
For many multi-robot problems, tasks are announced during execution, where task announcement times and locations are uncertain. To synthesise multi-robot behaviour that is robust to early announcements and unexpected delays, multi-robot task allocation methods must explicitly model the stochastic processes that govern task announcement. In this paper, we model task announcement using continuous-time Markov chains which predict when and where tasks will be announced. We then present a task allocation framework which uses the continuous-time Markov chains to allocate tasks proactively, such that robots are near or at the task location upon its announcement. Our method seeks to minimise the expected total waiting duration for each task, i.e. the duration between task announcement and a robot beginning to service the task. Our framework can be applied to any multi-robot task allocation problem where robots complete spatiotemporal tasks which are announced stochastically. We demonstrate the efficacy of our approach in simulation, where we outperform baselines which do not allocate tasks proactively, or do not fully exploit our task announcement models
Counterexample Guided Abstraction Refinement with Non-Refined Abstractions for Multi-Agent Path Finding
Counterexample guided abstraction refinement (CEGAR) represents a powerful
symbolic technique for various tasks such as model checking and reachability
analysis. Recently, CEGAR combined with Boolean satisfiability (SAT) has been
applied for multi-agent path finding (MAPF), a problem where the task is to
navigate agents from their start positions to given individual goal positions
so that the agents do not collide with each other.
The recent CEGAR approach used the initial abstraction of the MAPF problem
where collisions between agents were omitted and were eliminated in subsequent
abstraction refinements. We propose in this work a novel CEGAR-style solver for
MAPF based on SAT in which some abstractions are deliberately left non-refined.
This adds the necessity to post-process the answers obtained from the
underlying SAT solver as these answers slightly differ from the correct MAPF
solutions. Non-refining however yields order-of-magnitude smaller SAT encodings
than those of the previous approach and speeds up the overall solving process
making the SAT-based solver for MAPF competitive again in relevant benchmarks
Oracles & Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement Learning
Stackelberg equilibria arise naturally in a range of popular learning
problems, such as in security games or indirect mechanism design, and have
received increasing attention in the reinforcement learning literature. We
present a general framework for implementing Stackelberg equilibria search as a
multi-agent RL problem, allowing a wide range of algorithmic design choices. We
discuss how previous approaches can be seen as specific instantiations of this
framework. As a key insight, we note that the design space allows for
approaches not previously seen in the literature, for instance by leveraging
multitask and meta-RL techniques for follower convergence. We propose one such
approach using contextual policies, and evaluate it experimentally on both
standard and novel benchmark domains, showing greatly improved sample
efficiency compared to previous approaches. Finally, we explore the effect of
adopting algorithm designs outside the borders of our framework
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Discourse annotation - Towards a dialogue system for pair programming
Le développement de systèmes de dialogue a fait l’objet d’une grande attention dans différents domaines. Avec les progrès récents des tâches de traitement du langage de programmation, les systèmes de dialogue destinés aux programmeurs deviennent un autre domaine d’application viable. Cependant, afin de développer un système de dialogue pour assister les programmeurs, il est nécessaire de traiter non seulement le code, mais aussi le langage naturel associé. Comment ces données doivent-elles être annotées ? Dans cet article, nous présentons une synthèse des méthodes les plus courantes d’annotation des dialogues, avec un accent particulier sur le domaine de la programmation. On considère d’abord les théories sur lesquelles ces méthodes sont basées, on énumère les principales méthodes et on analyse les particularités du domaine de la programmation et dans quelle mesure les principales méthodes d’annotation sont adaptées à ce domaine.
Much work has been carried out on dialogue system development in different fields. With recent advances in Programming Language Processing tasks, dialogue systems aimed at programmers are becoming another viable area of application. However, the data necessary for a dialogue system that can assist programmers involves not only code, but the natural language around it. How should this data be annotated? In this review we examine the most common approaches to dialogue annotation, paying special attention to programming settings. We first look at the broader theories that inform these approaches, and after our review of the most widely used annotation schemes we analyze the peculiarities of the programming context and how well suited the existing schemes are for this setting
On the Stability of Gated Graph Neural Networks
In this paper, we aim to find the conditions for input-state stability (ISS)
and incremental input-state stability (ISS) of Gated Graph Neural
Networks (GGNNs). We show that this recurrent version of Graph Neural Networks
(GNNs) can be expressed as a dynamical distributed system and, as a
consequence, can be analysed using model-based techniques to assess its
stability and robustness properties. Then, the stability criteria found can be
exploited as constraints during the training process to enforce the internal
stability of the neural network. Two distributed control examples, flocking and
multi-robot motion control, show that using these conditions increases the
performance and robustness of the gated GNNs
Search and Explore: Symbiotic Policy Synthesis in POMDPs
This paper marries two state-of-the-art controller synthesis methods for
partially observable Markov decision processes (POMDPs), a prominent model in
sequential decision making under uncertainty. A central issue is to find a
POMDP controller - that solely decides based on the observations seen so far -
to achieve a total expected reward objective. As finding optimal controllers is
undecidable, we concentrate on synthesising good finite-state controllers
(FSCs). We do so by tightly integrating two modern, orthogonal methods for
POMDP controller synthesis: a belief-based and an inductive approach. The
former method obtains an FSC from a finite fragment of the so-called belief
MDP, an MDP that keeps track of the probabilities of equally observable POMDP
states. The latter is an inductive search technique over a set of FSCs, e.g.,
controllers with a fixed memory size. The key result of this paper is a
symbiotic anytime algorithm that tightly integrates both approaches such that
each profits from the controllers constructed by the other. Experimental
results indicate a substantial improvement in the value of the controllers
while significantly reducing the synthesis time and memory footprint.Comment: Accepted to CAV 202
Adaptive Robotic Information Gathering via Non-Stationary Gaussian Processes
Robotic Information Gathering (RIG) is a foundational research topic that
answers how a robot (team) collects informative data to efficiently build an
accurate model of an unknown target function under robot embodiment
constraints. RIG has many applications, including but not limited to autonomous
exploration and mapping, 3D reconstruction or inspection, search and rescue,
and environmental monitoring. A RIG system relies on a probabilistic model's
prediction uncertainty to identify critical areas for informative data
collection. Gaussian Processes (GPs) with stationary kernels have been widely
adopted for spatial modeling. However, real-world spatial data is typically
non-stationary -- different locations do not have the same degree of
variability. As a result, the prediction uncertainty does not accurately reveal
prediction error, limiting the success of RIG algorithms. We propose a family
of non-stationary kernels named Attentive Kernel (AK), which is simple, robust,
and can extend any existing kernel to a non-stationary one. We evaluate the new
kernel in elevation mapping tasks, where AK provides better accuracy and
uncertainty quantification over the commonly used stationary kernels and the
leading non-stationary kernels. The improved uncertainty quantification guides
the downstream informative planner to collect more valuable data around the
high-error area, further increasing prediction accuracy. A field experiment
demonstrates that the proposed method can guide an Autonomous Surface Vehicle
(ASV) to prioritize data collection in locations with significant spatial
variations, enabling the model to characterize salient environmental features.Comment: International Journal of Robotics Research (IJRR). arXiv admin note:
text overlap with arXiv:2205.0642
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