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Simon Parsons

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134 papers
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134

JAAMAS Journal 2026 Journal Article

Game Theory and Decision Theory in Multi-Agent Systems

  • Simon Parsons
  • Michael Wooldridge

Abstract In the last few years, there has been increasing interest from the agent community in the use of techniques from decision theory and game theory. Our aims in this article are firstly to briefly summarize the key concepts of decision theory and game theory, secondly to discuss how these tools are being applied in agent systems research, and finally to introduce this special issue of Autonomous Agents and Multi-Agent Systems by reviewing the papers that appear.

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FLAP Journal 2025 Journal Article

Applications of Argumentation-based Dialogues

  • Andreas Xydis
  • Federico Castagna
  • Elizabeth I Sklar
  • Simon Parsons

Communication plays a pivotal role in social phenomena such as belief polar- ization, scientific inquiry, and collective problem-solving. Agent-Based Models (ABMs) are computational tools that simulate the emergence of macro-level phenomena from micro-level interactions among agents. This paper focuses on Argumentative Agent-Based Models (AABMs), a specialized subset of ABMs that study argumentative communication, where agents provide reasons to sup- port or counter opinions. We present a systematic overview of AABMs, detailing their design, methodologies, and applications across disciplines. Key research questions include understanding the dynamics of consensus versus polarization, the conditions for epistemic reliability in collective decision-making, and the mechanisms that foster efficient collaboration within diverse groups through ar- gumentative exchanges. By synthesizing contributions from computer science, social science, and philosophy, this paper serves as both an entry point for new- comers and a comprehensive resource for researchers advancing the study of AABMs. We are grateful to two anonymous reviewers for valuable feedback on an earlier draft of this entry. Many thanks also to Felix Kopecky and to Carlo Proietti for helpful comments.

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ICRA Conference 2024 Conference Paper

Acoustic Soft Tactile Skin (AST Skin)

  • Vishnu Rajendran S
  • Willow Mandil
  • Kiyanoush Nazari
  • Simon Parsons
  • Amir M. Ghalamzan E.

This paper presents a novel acoustic soft tactile (AST) skin technology operating with sound waves. In this innovative approach, the sound waves generated by a speaker travel in channels embedded in a soft membrane and get modulated due to a deformation of the channel when pressed by an external force and received by a microphone at the end of the channel. The sensor leverages regression and classification methods for estimating the normal force and its contact location. Our sensor can be affixed to any robot part, e. g. , end effectors or arm. We tested several regression and classifier methods to learn the relation between sound wave modulation, the applied force, and its location, respectively and picked the best-performing models for force and location predictions. The best skin configurations yield more than 93% of the force estimation within ±1. 5 N tolerances for a range of 0-30 +1 N and contact locations with over 96% accuracy. We also demonstrated the performance of AST Skin technology for a real-time gripping force control application.

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JAIR Journal 2024 Journal Article

Computational Argumentation-based Chatbots: A Survey

  • Federico Castagna
  • Nadin Kökciyan
  • Isabel Sassoon
  • Simon Parsons
  • Elizabeth Sklar

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.

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JAIR Journal 2023 Journal Article

A Model to Support Collective Reasoning: Formalization, Analysis and Computational Assessment

  • Jordi Ganzer
  • Natalia Criado
  • Maite Lopez-Sanchez
  • Simon Parsons
  • Juan A. Rodriguez-Aguilar

In this paper we propose a new model to represent human debates and methods to obtain collective conclusions from them. This model overcomes two drawbacks of existing approaches. First, our model does not assume that participants agree on the structure of the debate. It does this by allowing participants to express their opinion about all aspects of the debate. Second, our model does not assume that participants’ opinions are rational, an assumption that significantly limits current approaches. Instead, we define a weaker notion of rationality that characterises coherent opinions, and we consider different scenarios based on the coherence of individual opinions and the level of consensus. We provide a formal analysis of different opinion aggregation functions that compute a collective decision based on the individual opinions and the debate structure. In particular, we demonstrate that aggregated opinions can be coherent even if there is a lack of consensus and individual opinions are not coherent. We conclude with an empirical evaluation demonstrating that collective opinions can be computed efficiently for real-sized debates.

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TIST Journal 2023 Journal Article

Argument Schemes and a Dialogue System for Explainable Planning

  • Quratul-Ain Mahesar
  • Simon Parsons

Artificial Intelligence (AI) is being increasingly deployed in practical applications. However, there is a major concern whether AI systems will be trusted by humans. To establish trust in AI systems, there is a need for users to understand the reasoning behind their solutions. Therefore, systems should be able to explain and justify their output. Explainable AI Planning is a field that involves explaining the outputs, i.e., solution plans produced by AI planning systems to a user. The main goal of a plan explanation is to help humans understand reasoning behind the plans that are produced by the planners. In this article, we propose an argument scheme-based approach to provide explanations in the domain of AI planning. We present novel argument schemes to create arguments that explain a plan and its key elements and a set of critical questions that allow interaction between the arguments and enable the user to obtain further information regarding the key elements of the plan. Furthermore, we present a novel dialogue system using the argument schemes and critical questions for providing interactive dialectical explanations.

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IROS Conference 2022 Conference Paper

Beyond mAP: Towards practical object detection for weed spraying in precision agriculture

  • Adrian Salazar Gomez
  • Madeleine Darbyshire
  • Junfeng Gao
  • Elizabeth Sklar
  • Simon Parsons

The evolution of smaller and more powerful GPUs over the last 2 decades has vastly increased the opportunity to apply robust deep learning-based machine vision approaches to real-time use cases in practical environments. One exciting application domain for such technologies is precision agriculture, where the ability to integrate on-board machine vision with data-driven actuation means that farmers can make decisions about crop care and harvesting at the level of the individual plant rather than the whole field. This makes sense both economically and environmentally. This paper assesses the feasibility of precision spraying weeds via a comprehensive evaluation of weed detection accuracy and speed using two separate datasets, two types of GPU, and several state-of-the-art object detection algorithms. A simplified model of precision spraying is used to determine whether the weed detection accuracy achieved could result in a sufficiently high weed hit rate combined with a significant reduction in herbicide usage. The paper introduces two metrics to capture these aspects of the real-world deployment of precision weeding and demonstrates their utility through experimental results.

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IS Journal 2021 Journal Article

Applying Metalevel Argumentation Frameworks to Support Medical Decision Making

  • Nadin Kokciyan
  • Isabel Sassoon
  • Elizabeth Sklar
  • Sanjay Modgil
  • Simon Parsons

People are increasingly employing artificial intelligence as the basis for decision-support systems (DSSs) to assist them in making well-informed decisions. Adoption of DSS is challenging when such systems lack support, or evidence, for justifying their recommendations. DSSs are widely applied in the medical domain, due to the complexity of the domain and the sheer volume of data that render manual processing difficult. This article proposes a metalevel argumentation-based decision-support system that can reason with heterogeneous data (e. g. , body measurements, electronic health records, clinical guidelines), while incorporating the preferences of the human beneficiaries of those decisions. The system constructs template-based explanations for the recommendations that it makes. The proposed framework has been implemented in a system to support stroke patients and its functionality has been tested in a pilot study. User feedback shows that the system can run effectively over an extended period.

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FLAP Journal 2021 Journal Article

Argument Schemes and Dialogue Protocols: Doug Walton's Legacy in Artificial Intelligence.

  • Peter McBurney
  • Simon Parsons

This paper is intended to honour the memory of Douglas Walton (1942–2020), a Canadian philosopher of argumentation who died in January 2020. Walton’s contributions to argumentation theory have had a very strong influence on Artificial Intelligence (ai), particularly in the design of autonomous software agents able to reason and argue with one another, and in the design of protocols to govern such interactions. In this paper, we explore two of these contributions — argumentation schemes and dialogue protocols — by discussing how they may be applied to a pressing current research challenge in ai: the automated assessment of explanations for automated decision-making systems.

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EUMAS Conference 2020 Conference Paper

An Argumentation-Based Approach to Generate Domain-Specific Explanations

  • Nadin Kökciyan
  • Simon Parsons
  • Isabel Sassoon
  • Elizabeth Sklar
  • Sanjay Modgil

Abstract In argumentation theory, argument schemes are constructs to generalise common patterns of reasoning; whereas critical questions (CQs) capture the reasons why argument schemes might not generate arguments. Argument schemes together with CQs are widely used to instantiate arguments; however when it comes to making decisions, much less attention has been paid to the attacks among arguments. This paper provides a high-level description of the key elements necessary for the formalisation of argumentation frameworks such as argument schemes and CQs. Attack schemes are then introduced to represent attacks among arguments, which enable the definition of domain-specific attacks. One algorithm is articulated to operationalise the use of schemes to generate an argumentation framework, and another algorithm to support decision making by generating domain-specific explanations. Such algorithms can then be used by agents to make recommendations and to provide explanations for humans. The applicability of this approach is demonstrated within the context of a medical case study.

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AAMAS Conference 2019 Conference Paper

Computational Argumentation-based Clinical Decision Support

  • Martin Chapman
  • Panagiotis Balatsoukas
  • Mark Ashworth
  • Vasa Curcin
  • Nadin Kökciyan
  • Kai Essers
  • Isabel Sassoon
  • Sanjay Modgil

This demonstration highlights the design of the Consult system, a modular decision-support system (DSS) intended to help patients suffering from chronic conditions self-manage their treatments. The system takes input from multiple sources, including commercial wellness sensors and a patient’s electronic health record, to inform a computational argumentation engine that constructs weighted opinions using these inputs and knowledge about their sources, and uses an interaction agent driven by argumentation-based dialogue to respond to user queries.

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EUMAS Conference 2017 Conference Paper

Two Forms of Minimality in ASPIC ^+

  • Zimi Li
  • Andrea Cohen
  • Simon Parsons

Abstract Many systems of structured argumentation explicitly require that the facts and rules that make up the argument for a conclusion be the minimal set required to derive the conclusion. \(\textsc {aspic}^{\mathsf {+}}\) does not place such a requirement on arguments, instead requiring that every rule and fact that are part of an argument be used in its construction. Thus \(\textsc {aspic}^{\mathsf {+}}\) arguments are minimal in the sense that removing any element of the argument would lead to a structure that is not an argument. In this paper we discuss these two types of minimality and show how the first kind of minimality can, if desired, be recovered in \(\textsc {aspic}^{\mathsf {+}}\).

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JAAMAS Journal 2015 Journal Article

Evaluation of a trust-modulated argumentation-based interactive decision-making tool

  • Elizabeth I. Sklar
  • Simon Parsons
  • Jennifer Mangels

Abstract The interactive ArgTrust application is a decision-making tool that is based on an underlying formal system of argumentation in which the evidence that influences a recommendation, or conclusion, is modulated according to values of trust that the user places in that evidence. This paper presents the design and analysis of a user study which was intended to evaluate the effectiveness of ArgTrust in a collaborative human–agent decision-making task. The results show that users’ interactions with ArgTrust helped them consider their decisions more carefully than without using the software tool.

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AAMAS Conference 2013 Conference Paper

An Argumentation-Based Dialogue System for Human-Robot Collaboration

  • Mohammad Q. Azhar
  • Simon Parsons
  • Elizabeth Sklar

Human-robot collaboration may fail due to conflicts in beliefs or plans between cooperating partners, or due to robot errors. Dialogue is an intuitive way to resolve such conflicts due to miscommunication. The research demonstrated here explores the notion of using argumentation-based dialogue for human-robot interaction. The demonstration presents a proof-of-concept prototype of a logic-based dialogue framework grounded in argumentation theory that addresses the “what to say” problem in human-robot communication during a collaborative task. A simulated human-robot treasure hunt game is shown, where a robot searches for objects of interest in a region that is not accessible to a human and interacts with the human in order to interpret its sensor data and complete the task effectively.

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AAMAS Conference 2013 Conference Paper

Argtrust: Decision Making with Information From Sources of Varying Trustworthiness

  • Simon Parsons
  • Elizabeth Sklar
  • Jordan Salvit
  • Holly Wall
  • Zimi Li

This work aims to support decision making in situations where sources of information are of varying trustworthiness. Formal argumentation is used to capture the relationships between such information sources and conclusions drawn from them. A prototype implementation is demonstrated, applied to a problem from military decision making.

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AAMAS Conference 2013 Conference Paper

Enabling Human-Robot Collaboration via Argumentation

  • Elizabeth Sklar
  • Mohammad Q. Azhar
  • Todd Flyr
  • Simon Parsons

A case is made for logical argumentation as a means for enabling true collaboration between human and robot partners. The majority of human-robot systems involve interactions in which the robot is subordinate, and all high-level decision making is performed by the human. However, in order to enable human-robot partnerships, both parties must be able to participate in constructive dialogue where each party can present ideas, these are discussed, and a shared conclusion is agreed upon. Argumentation is a method that can support such needs, as outlined in this short paper.

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AAMAS Conference 2013 Conference Paper

HRTeam: A Framework to Support Research on Human/Multi-Robot Interaction

  • Elizabeth Sklar
  • Simon Parsons
  • A. Tuna Özgelen
  • Eric Schneider
  • Michael Costantino
  • Susan L. Epstein

The HRTeam framework supports research on discovering and evaluating methods for addressing a range of issues in human/multi-robot team interaction. Three sample tasks illustrate the methods currently being investigated: mission selection, dictated by a human operator; collision avoidance, taught by a human trainer; and targeted exploration, jointly achieved with a human collaborator. Physical and simulated multi-robot environments are used to support this research.

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AAMAS Conference 2013 Conference Paper

Maximizing Matching in Double-Sided Auctions

  • Jinzhong Niu
  • Simon Parsons

Traditionally in double auctions, offers are cleared at the equilibrium price. In this paper, we introduce a novel, non-recursive, matching algorithm for double auctions, which aims to maximize the amount of commodities to be traded. Our algorithm has lower time and space complexities than existing algorithms.

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JAAMAS Journal 2012 Journal Article

Learning strategies for task delegation in norm-governed environments

  • Chukwuemeka David Emele
  • Timothy J. Norman
  • Simon Parsons

Abstract How do I choose whom to delegate a task to? This is an important question for an autonomous agent collaborating with others to solve a problem. Were similar proposals accepted from similar agents in similar circumstances? What arguments were most convincing? What are the costs incurred in putting certain arguments forward? Can I exploit domain knowledge to improve the outcome of delegation decisions? In this paper, we present an agent decision-making mechanism where models of other agents are refined through evidence from past dialogues and domain knowledge, and where these models are used to guide future delegation decisions. Our approach combines ontological reasoning, argumentation and machine learning in a novel way, which exploits decision theory for guiding argumentation strategies. Using our approach, intelligent agents can autonomously reason about the restrictions (e. g. , policies/norms) that others are operating with, and make informed decisions about whom to delegate a task to. In a set of experiments, we demonstrate the utility of this novel combination of techniques. Our empirical evaluation shows that decision-theory, machine learning and ontology reasoning techniques can significantly improve dialogical outcomes.

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AAAI Conference 2011 Conference Paper

Approaches to Multi-Robot Exploration and Localization

  • Arif Ozgelen
  • Michael Costantino
  • Adiba Ishak
  • Moses Kingston
  • Diquan Moore
  • Samuel Sanchez
  • J. Munoz
  • Simon Parsons

We present approaches to several fundamental tasks in multirobot team-based exploration and localization, based on student projects developed in the past year.

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AAMAS Conference 2011 Conference Paper

Argumentation Strategies for Plan Resourcing

  • Chukwuemeka D. Emele
  • Timothy J. Norman
  • Simon Parsons

What do I need to say to convince you to do something? This is an important question for an autonomous agent deciding whom to approach for a resource or for an action to be done. Were similar requests granted from similar agents in similar circumstances? What arguments were most persuasive? What are the costs involved in putting certain arguments forward? In this paper we present an agent decision-making mechanism where models of other agents are refined through evidence from past dialogues, and where these models are used to guide future argumentation strategy. We empirically evaluate our approach to demonstrate that decision-theoretic and machine learning techniques can both significantly improve the cumulative utility of dialogical outcomes, and help to reduce communication overhead.

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EUMAS Conference 2011 Conference Paper

Argumentation Strategies for Task Delegation

  • Chukwuemeka David Emele
  • Timothy J. Norman
  • Simon Parsons

Abstract What argument(s) do I put forward in order to persuade another agent to do something for me? This is an important question for an autonomous agent collaborating with others to solve a problem. How effective were similar arguments in convincing similar agents in similar circumstances? What are the risks associated with putting certain arguments forward? Can agents exploit evidence derived from past dialogues to improve the outcome of delegation decisions? In this paper, we present an agent decision-making mechanism where models of other agents are refined through evidence derived from dialogues, and where these models are used to guide future argumentation strategy. We combine argumentation, machine learning and decision theory in a novel way that enables agents to reason about constraints (e. g. , policies) that others are operating within, and make informed decisions about whom to delegate a task to. We demonstrate the utility of this novel approach through empirical evaluation in a plan resourcing domain. Our evaluation shows that a combination of decision-theoretic and machine learning techniques can significantly help to improve dialogical outcomes.

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AAMAS Conference 2011 Conference Paper

Argumentation-Based Reasoning in Agents with Varying Degrees of Trust

  • Simon Parsons
  • Yuqing Tang
  • Elizabeth Sklar
  • Peter McBurney
  • Kai Cai

In any group of agents, trust plays an important role. The degree to which agents trust one another will inform what they believe, and, as a result the reasoning that they perform and the conclusions that they come to when that involves information from other agents. In this paper we consider a group of agents with varying degrees of trust of each other, and examine the combinations of trust with the argumentation-based reasoning that they can carry out. The question we seek to answer is "What is the relationship between the trust one agent has in another and the conclusions that it can draw using information from that agent? ", and show that there are a range of answers depending upon the way that the agents deal with trust.

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AAMAS Conference 2011 Conference Paper

Probabilistic Hierarchical Planning over MDPs

  • Yuqing Tang
  • Felipe Meneguzzi
  • Katia Sycara
  • Simon Parsons

In this paper, we propose a new approach to using probabilistic hierarchical task networks (HTNs) as an effective method for agents to plan in conditions in which their problem-solving knowledge is uncertain, and the environment is non-deterministic. In such situations it is natural to model the environment as a Markov decision process (MDP). We show that using Earley graphs, it is possible to bridge the gap between HTNs and MDPs. We prove that the size of the Earley graph created for given HTNs is bounded by the total number of tasks in the HTNs and show that from the Earley graph we can then construct a plan for a given task that has the maximum expected value when it is executed in an MDP environment.

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AIJ Journal 2011 Journal Article

Weighted argument systems: Basic definitions, algorithms, and complexity results

  • Paul E. Dunne
  • Anthony Hunter
  • Peter McBurney
  • Simon Parsons
  • Michael Wooldridge

We introduce and investigate a natural extension of Dung's well-known model of argument systems in which attacks are associated with a weight, indicating the relative strength of the attack. A key concept in our framework is the notion of an inconsistency budget, which characterises how much inconsistency we are prepared to tolerate: given an inconsistency budget β, we would be prepared to disregard attacks up to a total weight of β. The key advantage of this approach is that it permits a much finer grained level of analysis of argument systems than unweighted systems, and gives useful solutions when conventional (unweighted) argument systems have none. We begin by reviewing Dung's abstract argument systems, and motivating weights on attacks (as opposed to the alternative possibility, which is to attach weights to arguments). We then present the framework of weighted argument systems. We investigate solutions for weighted argument systems and the complexity of computing such solutions, focussing in particular on weighted variations of grounded extensions. Finally, we relate our work to the most relevant examples of argumentation frameworks that incorporate strengths.

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AAMAS Conference 2010 Conference Paper

A Grey-Box Approach to Automated Mechanism Design

  • Jinzhong Niu
  • Kai Cai
  • Simon Parsons

This paper presents an approach to automated mechanism design inthe domain of double auctions. We describe a novel parameterizedspace of double auctions, and then introduce an evolutionary searchmethod that searches this space of parameters. The approach evaluates auction mechanisms using the framework of the TAC MarketDesign Game and relates the performance of the markets in thatgame to their constituent parts using reinforcement learning. Experiments show that the strongest mechanisms we found using thisapproach are able to win the Market Design Game against known, strong opponents.

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AAMAS Conference 2010 Conference Paper

Learning Policies through Argumentation-derived Evidence

  • Chukwuemeka Emele
  • Timothy Norman
  • Frank Guerin
  • Simon Parsons

We present an efficient approach for identifying, learningand modeling the policies of others during collaborative activities. In a set of experiments, we demonstrate that moreaccurate models of others' policies (or norms) can be developed more rapidly using various forms of evidence fromargumentation-based dialogue.

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AAMAS Conference 2009 Conference Paper

A Model for Integrating Dialogue and the Execution of Joint Plans

  • Yuqing Tang
  • Timothy J. Norman
  • Simon Parsons

Coming up with a plan for a team that operates in a non-deterministic environment is a complex process, and the problem is further complicated by the need for team members to communicate while the plan is being executed. Such communication is required, for example, to make sure that information critical to the plan is passed in time for it to be useful. In this paper we present a model for constructing joint plans for a team of agents that takes into account their communication needs. The model builds on recent developments in symbolic non-deterministic planning, ideas that have not previously been applied to this problem.

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JAAMAS Journal 2009 Journal Article

Evolutionary mechanism design: a review

  • Steve Phelps
  • Peter McBurney
  • Simon Parsons

Abstract The advent of large-scale distributed systems poses unique engineering challenges. In open systems such as the internet it is not possible to prescribe the behaviour of all of the components of the system in advance. Rather, we attempt to design infrastructure, such as network protocols, in such a way that the overall system is robust despite the fact that numerous arbitrary, non-certified, third-party components can connect to our system. Economists have long understood this issue, since it is analogous to the design of the rules governing auctions and other marketplaces, in which we attempt to achieve socially-desirable outcomes despite the impossibility of prescribing the exact behaviour of the market participants, who may attempt to subvert the market for their own personal gain. This field is known as “mechanism design”: the science of designing rules of a game to achieve a specific outcome, even though each participant may be self-interested. Although it originated in economics, mechanism design has become an important foundation of multi-agent systems (MAS) research. In a traditional mechanism design problem, analytical methods are used to prove that agents’ game-theoretically optimal strategies lead to socially desirable outcomes. In many scenarios, traditional mechanism design and auction theory yield clear-cut results; however, there are many situations in which the underlying assumptions of the theory are violated due to the messiness of the real-world. In this paper we review alternative approaches to mechanism design which treat it as an engineering problem and bring to bear engineering design principles, viz. : iterative step-wise refinement of solutions, and satisficing instead of optimization in the face of intractable complexity. We categorize these approaches under the banner of evolutionary mechanism design.

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AAMAS Conference 2009 Conference Paper

Inconsistency Tolerance in Weighted Argument Systems

  • Paul E. Dunne
  • Anthony Hunter
  • Peter McBurney
  • Simon Parsons
  • Michael Wooldridge

We introduce and investigate a natural extension of Dung’s wellknown model of argument systems in which attacks are associated with a weight, indicating the relative strength of the attack. A key concept in our framework is the notion of an inconsistency budget, which characterises how much inconsistency we are prepared to tolerate: given an inconsistency budget β, we would be prepared to disregard attacks up to a total cost of β. The key advantage of this approach is that it permits a much finer grained level of analysis of argument systems than unweighted systems, and gives useful solutions when conventional (unweighted) argument systems have none. We begin by reviewing Dung’s abstract argument systems, and present the model of weighted argument systems. We then investigate solutions to weighted argument systems and the associated complexity of computing these solutions, focussing in particular on weighted variations of grounded extensions.

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ICRA Conference 2009 Conference Paper

Learning to stabilize the head of a quadrupedal robot with an artificial vestibular system

  • Marek Marcinkiewicz
  • Ravi Kaushik
  • Igor Labutov
  • Simon Parsons
  • Theodore Raphan

During quadrupedal robot locomotion, there is pitch, yaw, and roll of the head and body due to the stepping. The head motion adversely affects visual sensors embedded in the robot's head. Mammals stabilize the head using a vestibulocollic reflex that detects linear and rotational acceleration. In this paper we describe the use of a machine learning algorithm that utilizes signals from an artificial vestibular system that has been embedded in the robot's head. Our approach can rapidly learn to compensate for the head movements that appear when no stabilization mechanism is present. The stabilization using a Sony Aibo robot occurs in only a few gait cycles.

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AAMAS Conference 2008 Conference Paper

Characterizing effective auction mechanisms: Insights from the 2007 TAC market design competition

  • Jinzhong Niu
  • Kai Cai
  • Enrico Gerding
  • Peter McBurney
  • Simon Parsons

This paper analyzes the entrants to the 2007 TAC Market Design competition. It presents a classification of the entries to the competition, and uses this classification to compare these entries. The paper also attempts to relate market dynamics to the auction rules adopted by these entries and their adaptive strategies via a set of post-tournament experiments. Based on this analysis, the paper speculates about the design of effective auction mechanisms, both in the setting of this competition and in the more general case.

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AAMAS Conference 2007 Conference Paper

An Agent-Based Model that Relates Investment in Education to Economic Prosperity

  • Yuqing Tang
  • Simon Parsons
  • Elizabeth Sklar

We describe work on an agent-based model that captures the relationship between the investment that a society makes in education and the outcome in terms of the health of the society's economy. In this work we created an agent-based version of an equation-based model from the economics literature, and explored various settings for parameters that control the behaviors of the agents and their environment.

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AIJ Journal 2007 Journal Article

An application of formal argumentation: Fusing Bayesian networks in multi-agent systems

  • Søren Holbech Nielsen
  • Simon Parsons

We consider a multi-agent system where each agent is equipped with a Bayesian network, and present an open framework for the agents to agree on a possible consensus network. The framework builds on formal argumentation, and unlike previous solutions on graphical consensus belief, it is sufficiently general to allow for a wide range of possible agreements to be identified.

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ICRA Conference 2007 Conference Paper

Implementation of Bio-Inspired Vestibulo-Ocular Reflex in a Quadrupedal Robot

  • Ravi Kaushik
  • Marek Marcinkiewicz
  • Jizhong Xiao
  • Simon Parsons
  • Theodore Raphan

Studies of primate locomotion have shown that the head and eyes are stabilized in space through the vestibulo-collic and vestibulo ocular reflexes (VCR, VOR). The VOR is a reflex eye movement control system that stabilizes the image on the retina of the eye during head movements in space. This stabilization helps maintain objects of interest approximately fixed on the retina during locomotion. In this paper we present the design and implementation of an artificial vestibular system, which drives a fully articulated binocular vision system for quadrupedal robots to maintain accurate gaze. The complete robot head has 9 degrees of freedom (DOF): pitch, yaw, and roll for the head and 3 DOF for left and right cameras. The SONY AIBOreg quadruped robot has been modified with additional hardware to emulate the vestibular system and the vestibulo-ocular reflex in primates.

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AAMAS Conference 2007 Conference Paper

On the Relevance of Utterances in Formal Inter-Agent Dialogues

  • Simon Parsons
  • Peter McBurney
  • Elizabeth Sklar
  • Michael Wooldridge

Work on argumentation-based dialogue has defined frameworks within which dialogues can be carried out, established protocols that govern dialogues, and studied di erent properties of dialogues. This work has established the space in which agents are permitted to interact through dialogues. Recently, there has been increasing interest in the mechanisms agents might use to choose how to act–the rhetori- cal manoeuvring that they use to navigate through the space defined by the rules of the dialogue. Key in such considerations is the idea of relevance, since a usual requirement is that agents stay focussed on the subject of the dialogue and only make relevant remarks. Here we study several notions of relevance, showing how they can be related to both the rules for carrying out dialogues and to rhetorical manoeuvring.

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AIJ Journal 2007 Journal Article

What evolutionary game theory tells us about multiagent learning

  • Karl Tuyls
  • Simon Parsons

This paper discusses If multi-agent learning is the answer, what is the question? [Y. Shoham, R. Powers, T. Grenager, If multi-agent learning is the answer, what is the question? Artificial Intelligence 171 (7) (2007) 365–377, this issue] from the perspective of evolutionary game theory. We briefly discuss the concepts of evolutionary game theory, and examine the main conclusions from [Y. Shoham, R. Powers, T. Grenager, If multi-agent learning is the answer, what is the question? Artificial Intelligence 171 (7) (2007) 365–377, this issue] with respect to some of our previous work. Overall we find much to agree with, concluding, however, that the central concerns of multiagent learning are rather narrow compared with the broad variety of work identified in [Y. Shoham, R. Powers, T. Grenager, If multi-agent learning is the answer, what is the question? Artificial Inteligence 171 (7) (2007) 365–377, this issue].

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KER Journal 2004 Journal Article

The synergy of electronic commerce, agents, and semantic Web services

  • M. BRIAN BLAKE
  • Simon Parsons
  • TERRY R. PAYNE

Advancements in software agents and Semantic Web service technologies are generally enhancing the landscape of electronic commerce. Semantic Web service technologies promise the standardisation and discoverability of software capabilities for network-enabled organisations. Moreover, with the addition of the intelligence and autonomy of software agents, transactions may be equally automated for consumer-to-consumer, business-to-consumer, and business-to-business collaborations. The 2003 Workshop on Electronic Commerce, Agents, and Semantic Web Services was held in conjunction with the International Conference on Electronic Commerce (ICEC2003). The purpose of this workshop was to bring together researchers and practitioners in the areas of electronic commerce, agents, and Semantic Web services to discuss the state-of-art in each individual area in addition to the synergies among the areas. This paper contains a summary of the workshop presentations and a discussion of next steps for Semantic Web services created in the working sessions concluding the workshop.

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KER Journal 2003 Journal Article

Argumentation-based negotiation

  • Iyad Rahwan
  • Sarvapali D. Ramchurn
  • Nicholas R. Jennings
  • Peter McBurney
  • Simon Parsons
  • Liz Sonenberg

Negotiation is essential in settings where autonomous agents have conflicting interests and a desire to cooperate. For this reason, mechanisms in which agents exchange potential agreements according to various rules of interaction have become very popular in recent years as evident, for example, in the auction and mechanism design community. However, a growing body of research is now emerging which points out limitations in such mechanisms and advocates the idea that agents can increase the likelihood and quality of an agreement by exchanging arguments which influence each others' states. This community further argues that argument exchange is sometimes essential when various assumptions about agent rationality cannot be satisfied. To this end, in this article, we identify the main research motivations and ambitions behind work in the field. We then provide a conceptual framework through which we outline the core elements and features required by agents engaged in argumentation-based negotiation, as well as the environment that hosts these agents. For each of these elements, we survey and evaluate existing proposed techniques in the literature and highlight the major challenges that need to be addressed if argument-based negotiation research is to reach its full potential.

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JELIA Conference 2002 Conference Paper

An Argumentation Framework for Merging Conflicting Knowledge Bases

  • Leila Amgoud
  • Simon Parsons

Abstract The problem of merging multiple sources of information is central in many information processing areas such as databases integrating problems, multiple criteria decision making, etc. Recently several approaches have been proposed to merge classical propositional bases. These approaches are in general semantically defined. They use priorities, generally based on Dalal’s distance for merging classical conflicting bases and return a new classical base as a result. In this paper, we present an argumentation framework for solving conflicts which could be applied to conflicts arising between agents in a multi-agent system. We suppose that each agent is represented by a consistent knowledge base and that the different agents are conflicting. We show that by selecting an appropriate preference relation between arguments, that framework can be used for merging conflicting bases and recovers the results of the different approaches proposed for merging bases [ 8 ], [ 12 ], [ 14 ], [ 13 ], [ 16 ], [ 17 ].

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KER Journal 2002 Journal Article

Book Review

  • Simon Parsons

Bayesian Networks and Decision Graphs by Finn V. Jensen, Springer Verlag, £64.95, ISBN 0-387-95259-4

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AIJ Journal 2002 Journal Article

Context-specific sign-propagation in qualitative probabilistic networks

  • Silja Renooij
  • Linda C. van der Gaag
  • Simon Parsons

Qualitative probabilistic networks are qualitative abstractions of probabilistic networks, summarising probabilistic influences by qualitative signs. As qualitative networks model influences at the level of variables, knowledge about probabilistic influences that hold only for specific values cannot be expressed. The results computed from a qualitative network, as a consequence, can be weaker than strictly necessary and may in fact be rather uninformative. We extend the basic formalism of qualitative probabilistic networks by providing for the inclusion of context-specific information about influences and show that exploiting this information upon reasoning has the ability to forestall unnecessarily weak results.

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UAI Conference 2002 Conference Paper

Formalizing Scenario Analysis

  • Peter McBurney
  • Simon Parsons

We propose a formal treatment of scenarios in the context of a dialectical argumentation formalism for qualitative reasoning about uncertain propositions. Our formalism extends prior work in which arguments for and against uncertain propositions were presented and compared in interaction spaces called Agoras. We now define the notion of a scenario in this framework and use it to define a set of qualitative uncertainty labels for propositions across a collection of scenarios. This work is intended to lead to a formal theory of scenarios and scenario analysis.

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KER Journal 2000 Journal Article

Game theoretic and decision theoretic agents

  • Simon Parsons
  • Michael Wooldridge

In the last few years, increasing numbers of members of the agent community have been adopting techniques from game theory and decision theory. Broadly speaking, decision theory (Raiffa, 1968) is a means of analysing which of a series of options should be taken when it is uncertain exactly what the result of taking the option will be. Decision theory concentrates on identifying the “best” decision option, where the notion of “best” is allowed to have a number of different meanings, of which the most common is that which maximises the expected utility of the decision maker. Game theory (Binmore, 1992) can be considered as a variant of decision theory in which the outcome of taking a particular decision is dependent upon the actions of another, frequently an opponent which is trying to maximise its own benefit at the cost of the decision maker. Alternatively, game theory can be considered a mechansim for analysing games between two players in which each gets to choose a move from some limited set of options and, depending on what both have chosen, each receives a payout. Since the payout one player receives depends upon the move made by the other then, to maximise its payout, each player needs to take into account the likely move taken by its opponent. From this perspective, decision theory can be considered to be the study of games played against nature, an opponent which does not look to gain the best payout, but rather acts randomly.

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UAI Conference 2000 Conference Paper

Pivotal Pruning of Trade-offs in QPNs

  • Silja Renooij
  • Linda C. van der Gaag
  • Simon Parsons
  • Shaw Green

Qualitative probabilistic networks have been designed for probabilistic reasoning in a qualitative way. Due to their coarse level of representation detail, qualitative probabilistic networks do not provide for resolving trade-offs and typically yield ambiguous results upon inference. We present an algorithm for computing more insightful results for unresolved trade-offs. The algorithm builds upon the idea of using pivots to zoom in on the trade-offs and identifying the information that would serve to resolve them.

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UAI Conference 2000 Conference Paper

Risk Agoras: Dialectical Argumentation for Scientific Reasoning

  • Peter McBurney
  • Simon Parsons

We propose a formal framework for intelligent systems which can reason about scientific domains, in particular about the carcinogenicity of chemicals, and we study its properties. Our framework is grounded in a philosophy of scientific enquiry and discourse, and uses a model of dialectical argumentation. The formalism enables representation of scientific uncertainty and conflict in a manner suitable for qualitative reasoning about the domain.

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IJCAI Conference 1999 Conference Paper

Maximum Entropy and Variable Strength Defaults

  • Rachel A. Bourne
  • Simon Parsons

A new algorithm for computing the maximum entropy ranking over models is presented. The algorithm handles arbitrary sets of propositional defaults with associated strength assignments and succeeds whenever the set satisfies a robustness condition. Failure of this condition implies the problem may not be sufficiently specified for a unique solution to exist. This work extends the applicability of the maximum entropy approach detailed in [Goldszmidt et a/. , 1993]) and clarifies the assumptions on which the method is based.

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KER Journal 1999 Journal Article

Special issue: Perspectives on intelligent agents research... one year later

  • Simon Parsons
  • Adele Howe

A year ago, we published a special issue of The Knowledge Engineering Review entitled "Perspectives on Recent Intelligent Agents Research as Viewed through Two Conferences". Our intention was to provide a snapshot of current research on intelligent agents - an area which continues to grow apace - by looking at the work presented at the 1997 instantiations of two of the major events in the Agent calendar. These were the International Conference on Autonomous Agents (Agents '97) and the International Conference and Exhibition on the Practical Application of Intelligent Agents and Multi-Agent Technology (PAAM '97). The result was a group of six papers which together spanned the main areas covered by the conferences as well as identifying some of the challenges faced by the agents community as a whole. In particular, we had summaries by the organisers of both of the conferences, speculation on the future of robotics, surveys of expert assistants and electronic commerce, and a discussion of how agents could be used in workflow management.

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KER Journal 1995 Journal Article

Information processing and the management of uncertainty

  • Simon Parsons
  • Alessandro Saffiotti

The First International Conference on Information Processing and the Management of Uncertainty (IPMU) was held in 1986 at a time of great debate about the necessity of modelling uncertainty in intelligent systems (which at that time largely meant rule-based expert systems) and the best way of doing so. Whereas the founders of the Conference on Uncertainty in Artificial Intelligence (UAI) in the United States set out with the aim of promoting the use of probability, the organisers of IPMU chose a diametrically opposed course. Though there were a few papers on probability at IPMU '86, the main focus was on alternative methods, primarily those based upon fuzzy sets. Though subsequent conferences have seen greater mix of papers, IPMU remains largely non-probabilistic with the result that the bulk of the participants come from Europe rather than the United States (despite the large amount of work on uncertainty, and especially probability, that is carried out in the US) making IPMU something of a counterpoint to UAT. The difference in participation is exacerbated by the location—whilst the UAI remains in North America, IPMU alternates between Paris and other cities in Europe, including Urbino in 1988 and Palma in 1992.

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UAI Conference 1995 Conference Paper

Refining reasoning in qualitative probabilistic networks

  • Simon Parsons

In recent years there has been a spate of papers describing systems for probabilisitic reasoning which do not use numerical probabilities. In some cases the simple set of values used by these systems make it impossible to predict how a probability will change or which hypothesis is most likely given certain evidence. This paper concentrates on such situations, and suggests a number of ways in which they may be resolved by refining the representation.

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KER Journal 1994 Journal Article

Textbooks for artificial intelligence Essentials of artificial intelligence by Matt Ginsburg, Morgan Kaufmann, 1993, £26.50, pp 430, ISBN 1-55860-221-6 - Formal concepts in artificial intelligence—fundamentals by Rajjan Shinghal, Chapman & Hall, 1992, £19.95, pp 666, ISBN 0-412-40790-6 - Artificial intelligence—structures and strategies for complex problem solving by George F. Luger and William A. Stubblefield, Benjamin/Cummings, 1993, pp 740, ISBN 0-8053-4780-1 - Knowledge engineering, Volume 1, Fundamentals edited by Hojjat Adeli, McGraw Hill, 1990, £21.95, pp 345, ISBN 0-07-000355-6 - Knowledge engineering, Volume 2, Applications edited by Hojjat Adeli, McGraw Hill, 1990, £31.95, pp 352, ISBN 0-07-000355-7

  • Simon Parsons
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KER Journal 1994 Journal Article

Uncertainty in artificial intelligence

  • Simon Parsons

The first conference on Uncertainty in Artificial Intelligence was held in 1985 by a group of people who felt that their views on the use of probability theory were not receiving a fair hearing from the rest of the Al community. At the time, mainstream opinion held that computational complexity of, and the amount of data required by, probabilistic methods made them inappropriate for realistic applications. As a result, those who claimed that probability theory was an adequate, if not the only adequate, method of handling uncertainty received a somewhat frosty reception.

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UAI Conference 1993 Conference Paper

On reasoning in networks with qualitative uncertainty

  • Simon Parsons
  • E. H. Mamdani

In this paper some initial work towards a new approach to qualitative reasoning under uncertainty is presented. This method is not only applicable to qualitative probabilistic reasoning, as is the case with other methods, but also allows the qualitative propagation within networks of values based upon possibility theory and Dempster-Shafer evidence theory. The method is applied to two simple networks from which a large class of directed graphs may be constructed. The results of this analysis are used to compare the qualitative behaviour of the three major quantitative uncertainty handling formalisms, and to demonstrate that the qualitative integration of the formalisms is possible under certain assumptions.

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EAAI Journal 1992 Journal Article

Qualitative, semiqualitative and interval algebras, and their application to engineering problems

  • Simon Parsons
  • Mirko Dohnal

This paper proposes the use of semiqualitative modelling for reasoning about the behaviour of complex physical systems. Semiqualitative modelling is a generalization of qualitative modelling which refines the set of intervals that values may be expressed in. Semiqualitative algebras are introduced, their most important features discussed, and related to qualitative algebras. The advantages that semiqualitative modelling offers over qualitative modelling are demonstrated by the solution of an example from the field of biotechnology. Finally, interval algebras are introduced as a generalization of semiqualitative algebras, and it is proved that it is possible to switch between different interval algebras in the course of computation in order to preserve the greatest possible degree of precision.

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