Program

The workshop will be held at MCEC 208 on 21st August Monday, 2017.

• Bo An  is a Nanyang Assistant Professor with the School of Computer Science and Engineering, Nanyang Technological University, Singapore. He received the Ph.D degree in Computer Science from the University of Massachusetts, Amherst. His current research interests include artificial intelligence, multiagent systems, game theory, and optimization. He has published over 70 referred papers at AAMAS, IJCAI, AAAI, ICAPS, KDD, JAAMAS, AIJ and ACM/IEEE Transactions. Dr. An was the recipient of the 2010 IFAAMAS Victor Lesser Distinguished Dissertation Award, an Operational Excellence Award from the Commander, First Coast Guard District of the United States, the Best Innovative Application Paper Award at AAMAS-12, the 2012 INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice, and the Innovative Application Award at IAAI-16. He was invited to give Early Career Spotlight talk at IJCAI-17. He led the team HogRider which won the 2017 Microsoft Collaborative AI Challenge. He is a member of the editorial board of JAIR and the Associate Editor of JAAMAS. He was elected to the board of directors of IFAAMAS.
  • Title of his talk: Game theoretic analysis of security and sustainability
  • Abstract: Computational game theory has become a powerful tool to address critical issues in security and sustainability. Casting the security resource allocation problem as a Stackelberg game, novel algorithms have been developed to provide randomized security resource allocations. These algorithms have led to deployed security-game based decision aids for many real-world security domains including infrastructure security and wildlife protection. We contribute to this community by addressing several major research challenges in complex security resource allocation, including dynamic payoffs, uncertainty, protection externality, games on networks, and strategic secrecy. We also analyze optimal security resource allocation in many potential application domains including cyber security. Furthermore, we apply game theory to reasoning optimal policy in deciding taxi pricing scheme and EV charging placement and pricing.
• Katsuhide Fujita is an Associate Professor of Institute of Engineering, Tokyo University of Agriculture and Technology. He received the B.E., M.E, and Doctor of Engineering from the Nagoya Institute of Technology. His main research interests include automated negotiation, multi-agent system, and decision support systems.The list of talks: 
  • Title: Compromising Strategies based on Opponent Modeling for Multi-issue Closed Negotiation
  • Abstract:  Multi-issue closed negotiation has attracted attention in the multi-agent system. Especially, multi-times, multi-lateral, nonlinear negotiations are important studies in multi-issue closed negotiations. In addition, an automated negotiating agent needs to have strategies for estimating opponent's utility function by learning from opponent's behaviors through negotiations since opponent's utility information is not open to others. This talk focuses on compromising negotiation strategies based on opponent modeling using multi-objective optimization and machine learning, and conflict modelling.  The effectiveness of these proposed methods is demonstrated in some realistic multi-issue closed negotiation scenarios.
• Enrico Gerding is an Associate Professor in the Agents, Interaction and Complexity (AIC) research group in the Department of Electronics and Computer Science (ECS) at the University of Southampton. He has been an academic at Southampton since 2007. He received his PhD from the Dutch National Center of Mathematics and Computer Science (CWI) in 2004 on the topic of automated negotiation. With around 100 peer-reviewed publications in high quality conferences, journals and books, he has an extensive track record in research on artificial intelligence, specifically the area of autonomous agents and multi-agent systems. This concerns developing software programs which can make autonomous decisions on behalf of users based on their preferences. 
  • Title: Agent-Based Privacy Permission Management using Automated Negotiation
  • Abstract:  Increasingly, our personal data is being collected by online services, whether this is through the web, smartphones, or Internet of Things  devices. As a result, we are constantly being asked to accept legally couched terms and conditions, or give permissions for data use in mobile apps.  However, these modes of obtaining consent are inadequate because: (1) they preclude ready understanding about what data is being collected and how it will be used, and (2) they do not offer a realistic choice. Such consent is therefore not meaningful. In this talk I will discuss progress on a project which attempts to address this growing concern. In particular, I will discuss how agent-based negotiation can be used to better control access and usage of data. The goal in doing so is to satisfy seemingly contradictory aims of minimising the cognitive burden yet obtaining agreements which are meaningful. 
• Johnathan Mell is a graduate research assistant and PhD student at University of Southern California's Department of Computer Science. He works under the supervision of Jonathan Gratch at the USC Institute for Creative Technologies. Johnathan is a part of the ICT emotion group, where he designs more human-like computers for a variety of applications. His undergraduate work was completed at the University of Pennsylvania, where he received his Bachelor's degrees in Economics (Entrepreneurial Management) from the Wharton School, and in Computer Engineering, from the School of Engineering and Applied Science in 2013. His undergraduate research was focused primarily on psychophysiological channels for game feedback mechanisms and on frustration in games and its effect on mood. Johnathan’s current research focuses on the impact of social features of repeated negotiations with a computer partner.  His work covers favor exchange, cross-cultural features, and temporal effects in an effort to make automated negotiators and emotive and realistic virtual characters. He is also interested in efficient designs for systems that are used by a non-AI "man behind the curtain", called "Wizard of Oz" systems.  To investigate these questions, he has developed the IAGO platform, which serves as a framework for creating Virtual Agents that negotiate with humans. Johnathan is published at AAMAS, IJCAI, AAAI, and ACII, and his IAGO platform was a finalist for Best Demonstration at AAMAS 2016.  He is a recipient of the USC Merit Top-Off Fellowship, and is a student member of AAAI, IEEE, and IGDA.  He has previously worked on human interface and training platforms for Disney Engineering, and intends to continue working at the intersection of entertainment, computation, and human interfaces.
  • Title of his talk: Human-Like Agents for Repeated, Social Negotiation
  • Abstract: Human-computer negotiation is fundamentally more similar to human-human negotiation than traditional agent-agent negotiation.  To design agents that are capable of being negotiation partners, teachers, and competitors, an incremental model of agent design is required.  In this review of relevant work, we identify “best practices” from human-human negotiation literature and show how these social techniques can be used to create more effective and realistic computer agents using the IAGO platform.  Building upon this, we identify improvements to social agents that allow them to account for individual differences in human competitors, temporal memory and relationship building, and eventually, community cognizance and positioning.  Results are presented from a number of represented studies, both published and ongoing, and the cross-disciplinary ramifications of the work are explored as it pertains to the research community at large.
• Ariel Rosenfeld  is a Koshland Postdoctoral Fellow at the Department of Computer Science and Applied Mathematics at Weizmann Institute of Science, Israel. He obtained a BSc in Computer Science and Economics, graduated  `magna cum laude', from Tel-Aviv University, Israel and a PhD in Computer Science form Bar-Ilan University, Israel. Rosenfeld's research focus is Human-Agent Interaction and he has published on the topic at top venues such as AAAI, IJCAI, AAMAS and ECAI. Rosenfeld also has a rich lecturing background, spanning over a decade, and he is currently acting as a lecturer at Bar-Ilan University, Israel.
  • Title of his talk: Predicting Human Decision-Making: Tools of the Trade
  • Abstract: Human decision-making often transcends our formal models of ``rationality". Designing intelligent agents that interact proficiently with people necessitates the modeling of human behavior and the prediction of their decisions. In this talk, we will focus on the prediction of human decision-making and its use in designing intelligent human-aware automated agents of varying natures; from purely conflicting interaction settings (e.g., security and games) to fully cooperative interaction settings (e.g., advise provision and human rehabilitation). We will present computational representations, algorithms and empirical methodologies for meeting the challenges that arise from the above tasks in both a single interaction (one-shot) and repeated interaction settings. 
• Tuomas Sandholm is Professor at Carnegie Mellon University in the Computer Science Department, with affiliate professor appointments in the Machine Learning Department, Ph.D. Program in Algorithms, Combinatorics, and Optimization (ACO), and CMU/UPitt Joint Ph.D. Program in Computational Biology. He is the Founder and Director of the Electronic Marketplaces Laboratory. He has published over 450 papers. He has built optimization-powered electronic marketplaces since 1989, and has fielded several of his systems. In parallel with his academic career, he was Founder, Chairman, and CTO/Chief Scientist of CombineNet, Inc. from 1997 until its acquisition in 2010. During this period the company commercialized over 800 of the world's largest-scale generalized combinatorial multi-attribute auctions, with over $60 billion in total spend and over $6 billion in generated savings. He is Founder and CEO of Optimized Markets, Inc., which is bringing a new paradigm to advertising campaign sales and scheduling - in TV (linear and digital), Internet display, mobile, game, radio, and cross-media advertising. His algorithms also run the UNOS kidney exchange, which includes 159 transplant centers. He has developed the leading algorithms for several general classes of game. The team that he leads is the current two-time world champion in computer Heads-Up No-Limit Texas Hold’em poker, and Libratus became the first and only AI to beat top humans at that game. He is Founder and CEO of Strategic Machine, Inc., which provides solutions for strategic reasoning under imperfect information in a broad set of applications ranging from poker to other recreational games to business strategy, negotiation, strategic pricing, finance, cybersecurity, physical security, military, auctions, political campaigns, and medical treatment planning. He served as the redesign consultant of Baidu’s sponsored search auctions and display advertising markets. He has served as consultant, advisor, or board member for Yahoo!, Google, Chicago Board Options Exchange, swap.com, Granata Decision Systems, and others. He holds a Ph.D. and M.S. in computer science and a Dipl. Eng. (M.S. with B.S. included) with distinction in Industrial Engineering and Management Science. Among his many honors are the NSF Career Award, inaugural ACM Autonomous Agents Research Award, Sloan Fellowship, Carnegie Science Center Award for Excellence, Edelman Laureateship, and IJCAI Computers and Thought Award. He is Fellow of the ACM, AAAI, and INFORMS. He holds an honorary doctorate from the University of Zurich.
  • Title of his talk: Sample Complexity of Multi-Item Profit Maximization
  • Abstract: The allocation of items among multiple agents when agents have combinatorial preferences is a central challenge in multiagent systems. Data-driven (typically automated) mechanism design is an emerging field in academic research with tight connections to industry where companies use customers’ purchase histories to design pricing mechanisms and auctions. We study data-driven profit maximization in the setting where the mechanism designer has samples from the distribution over buyers’ values [Likhodedov & Sandholm AAAI-04, AAAI-05;Sandholm & Likhodedov Operations Research 2015]. We thus relax the traditional assumption that the designer knows this distribution.  The designer can estimate a mechanism’s expected profit over this distribution by its average profit over a set of samples. We provide an overarching theorem that bounds the number of samples the designer needs to ensure that the difference between empirical and expected profit is small for many natural, deterministic, well-studied pricing and auction mechanisms. Furthermore, there is a tradeoff in a mechanism’s complexity: more complex mechanisms often achieve higher profit than simpler mechanisms, but more samples are required to ensure empirical profit nearly matches expected profit. We provide techniques for optimizing this tradeoff. (Joint work with Nina Balcan and Ellen Vitercik.)
• Simeon Simoff  is currently the Dean of the School of Computing, Engineering and Mathematics at the Western Sydney University (WSU). Simeon was previously a Professor of Information Technology at the University of Technology, Sydney (UTS), where he established the e-Markets research program (research.it.uts.edu.au/emarkets/), currently running between WSU, UTS, UNSW and the Institute of Artificial Intelligence Research of the Spanish Research Council, Barcelona, Spain. This has been a reference lab in intelligent trading technology. Simeon is known for his transdisciplinary research which connects data science and analytics, human-computer systems and intelligent technologies. He is a pioneer in multimedia data mining, visual analytics and virtual design studios. He has published more than 350 research works, including the monograph on virtual design studios and the collection on visual data mining. Simeon is a founding Director and a Fellow of the Institute of Analytics Professionals of Australia, and Fellow of Engineers Australia. Currently he is an editor of the Australian Computer Society Conferences in Research and Practice in Information Technology. Prior to that he was Associate Editor of the American Society of Civil Engineering (ASCE) Journal of Computing in Civil Engineering. He is founder and chair of the ACM SIGKDD Multimedia Data Mining conference series MDM@KDD, the Australasian Data Mining Conference series AusDM, and the Visual Data Mining international workshop series.
  • Title of his talk:Information Revelation + A&C Reasoning + Argumentation
  • Abstract: Mediation is an important method in dealing with failed negotiations and/or dispute resolutions allowing negotiating parties to find solutions across the differences of their positions in a collaborative manner. This presentation provides an overview of and reflection on the work on mediation conducted as part of the development of the ‘curious negotiator’ technology. The approach treats mediation as a knowledge intensive process, based on the interplay of past experiences and information revelation with reasoning methods, allowing, if needed, to reframe negotiated problems and further collectively co-develop refined solutions through argumentation. The approaches and algorithms are illustrated with examples.