Automated Agents for Human Persuasion Amos Azariahas 2 papers
This thesis studies the computational complexity of different problems from three areas of social choice. The first one is voting, and especially the problem of determining whether a distinguished candidate can be a winner in an election with some kind of incomplete information. The second setting is in the broader sense related to the problem of determining winners. Here the computational complexity of problems related to minimal upward and downward covering sets are studied. The last area is judgment aggregation, where judges have to report their judgments over a set of possibly interconnected propositions and a collective judgment set is determined by some aggregation procedure. In contrast to the problems mentioned above we do not study the complexity of some kind of 'winner'-problem, but the complexity of two forms of influencing the outcome, namely manipulation and bribery. Computational Complexity in Three Areas of Computational Social Choice: Possible Winners, Unidirectional Covering Sets, and Judgment Aggregation Dorothea Baumeisterhas 2 papers
Firefly-Inspired Synchronization in Multi-Agent Systems Iva Bojic
Communication in Swarms of Miniature Mobile Robots Jianing Chenhas 2 papers
Automated negotiation agents that aim to proficiency negotiate in realistic scenarios involving humans in repeated environments have some special requirements; e.g. they must deal with emotions and uncertainty. In this paper we propose an architecture for agents to bilaterally negotiate on plans of action with several other agents (also humans). An Architecture for Multiple Bilateral Negotiation Angela Fabregueshas 2 papers
Reputation Mechanism for E-Commerce in Virtual Reality Environments Hui Fanghas 2 papers
A comprehensive conceptualization tool for agent-based social simulation would increase the acceptance of this approach among the social scientists similar to the exiting simulation approaches such as system dynamic and discrete event modelling. In this research we explore this proposition to provide an opportunity for the social scientist to access the vast body of research in multi-agent systems area. An agent-oriented social simulation methodology for institutional driven evolving agents Amineh Ghorbanihas 2 papers
Distributed Constraint Optimization Problems (DCOPs) are commonly used for modeling multi-agent coordination problems. DCOPs can be optimally solved by distributed search algorithms, based on messages exchange. In centralized solving, maintaining soft arc consistency techniques during search has proved to be beneficial for performance. In this thesis we aim to explore the maintenance of different levels of soft arc consistency in distributed search when solving DCOPs. Distributed Constraint Optimization Problems related with Soft Arc Consistency Patricia Gutierrezhas 3 papers
People's cultural background has been shown to act the way they reach agreements in negotiation and how they fulfil these agreements. This paper presents a novel agent design for negotiating with people from different cultures. Our setting involved an alternating-offer protocol that allowed parties to choose the extent to which they kept each of their agreements during the negotiation. A challenge to designing agents for such setting is to predict how people reciprocate their actions over time despite the scarcity of prior data of their behavior across different cultures. Our methodology addresses this challenge by combining a decision theoretic model with classical machine learning techniques to predict how people respond to offers, and the extent to which they fulfil agreements. The agent based its initial strategy on a general model of the population in each culture, and adapted its behavior to its particular partner over time. This agent was evaluated empirically by playing with 157 people in three countries (Lebanon, the U.S., and Israel) in which people are known to vary widely in their negotiation behavior. The agent was able to outperform people in all countries under conditions that varied how parties depended on each other at the onset of the negotiation. This is the first work to show that a computer agent can learn to outperform people when negotiating in three countries representing different cultures. A Cultural Sensitive Agent for Human-Computer Negotiation Galit Haimhas 2 papers
A Multiagent Evolutionary Framework for Complex Optimization Problems Siwei Jianghas 2 papers
Adaptive Agents on Evolving Networks Ardeshir Kianercyhas 2 papers
Sustainable energy domains have become extremely important due to the significant growth in energy usage. Building multiagent systems for real world energy applications raises several research challenges regarding scalability, multiple competing objectives to be optimized, model uncertainty, and complexity in deploying the system. Motivated by these challenges, my thesis proposes a new set of models and algorithms to conserve building energy. My thesis contributes to a very new area that requires considering largescale multi-objective optimization as well as uncertainty over occupant preferences when negotiating energy reduction. My work has shown significant potential for energy savings by investigating effective and tailored methods in the multiagent system. The suggested methods have been verified in a validated simulation testbed and included a human subject study in the real-world as a trial study. Multiagent Systems for Sustainable Energy Applications Jun-young Kwakhas 3 papers
Before robots can become a viable technology for assisting people with everyday tasks, they must be able to adapt to the ever-changing conditions found in real, human-centered environments, designed for a person's ability to walk on two legs. This dictates the need for humanoid robots that can exhibit robust, bipedal locomotion. This thesis explores different methods for developing and optimizing walks for humanoid robots. Generalizable Framework for Designing and Optimizing Dynamic, Robust, and Adaptive Bipedal Locomotion Patrick MacAlpinehas 2 papers
Due to highly constrained ageing infrastructure, current electricity networks will not be able to handle an increased amount of generation; particularly that from intermittent renewable resources deployed within distribution networks. In order to efficiently control this increased generation, de- centralised autonomous control is the only viable solution; due to the computational complexities that arise for large networks. Thus, an agent managed smart grid will be essential for future electricity networks. Part of this system will be agent managed distribution networks that incorporate an increased amount of decentralised micro-generation from renewable resources. My PhD focuses on the algorithms and techniques needed for distribution network operators to use in order to coordinate generators within their networks efficiently. Coordinating Generators in the Smart Grid Sam Millerhas 2 papers
This research presents a novel decision-theoretic approach to control and coordinate multiple active (pan-tilt-zoom) cameras to achieve high-quality surveillance. The decision-theoretic approaches provide robust mathematical frameworks that can model the interactions between the active camera network and the surveillance environment. It offers advantage of planning optimal control decisions for active cameras to achieve the desired surveillance task, in presence of uncertainties like targets' motion, location, etc. In this work, we provide an overview of proposed framework in which a surveillance tasks can be posed as stochastic optimization problems. Specifically we propose solutions for two novel problems/tradeoffs in active camera surveillance: (i) Maximizing the number of targets observed in active cameras while maintaining the guaranteed resolution of these targets and (ii) Maximizing the number of targets observed in active cameras while minimizing the location uncertainty of the targets that are currently not observed in any of the active cameras. By exploiting the structure and properties that are inherently present in the surveillance problem, we have reduced the exponential policy computation time to polynomial time. This makes the decision-theoretic approach feasible for the surveillance applications. Decision-Theoretic Approach for Controlling and Coordinating multiple Active Cameras in Surveillance Prabhu Natarajanhas 2 papers
My Ph.D thesis focuses on the study of solution concepts for rational learning agents in extensive-form games in absence of common knowledge; specifically, on the definition of solution concepts, their search, analysis of static and dynamic property, characterization of learning dynamics. Summarily, my work is finalized to better understand how to integrate more thoroughly game theory and machine learning. Game theoretical solution concepts for learning agents with extensive-form games Fabio Panozzohas 2 papers
Agent-Oriented Business Modeling Pankaj Telanghas 3 papers
Agent Support for Collaboration in Complex Deliberative Dialogues Alice Toniolohas 2 papers
Many real-world situations involve attempts to spread influence through a social network. For example, viral marketing is when a marketer selects a few people to receive some initial advertisement in the hopes that these 'seeds' will spread the news. Even peacekeeping operations in one area have been shown to have a contagious effect on the neighboring vicinity. Each of these domains also features multiple parties seeking to maximize or mitigate a contagious effect by spreading its own influence among a select few seeds, naturally yielding an adversarial resource allocation problem. As past researchers of security resource allocation have done, I propose using game theory to develop such policies and model the interconnected network of people as a graph. Unlike this past work in security games, however, actions in these domains possess a probabilistic, non-local impact that makes even payoff determination an NP-Hard problem. My thesis proposes novel techniques for solving this type of game for real-world problem sizes by building upon the latest research in security games and influence blocking maximization. I have also advanced the understanding of contagion phenomena by developing empirical evaluation methods for computational contagion models. Finally, my thesis formalizes an entirely new class of security games with wide-ranging applications from marketing to peacekeeping. Security Games and Contagion Jason Tsaihas 2 papers
Forming effective coalitions is a major research challenge in AI and multi-agent systems. Thus, cooperative games, including Coalition Structure Generation (CSG), have been attracting considerable attention from the AI research community. Traditionally, the input of a cooperative game is a black-box function called a characteristic function. Therefore, many problems in cooperative games, including CSG, tend to be computationally intractable. In my thesis, I develop new representation schemes, which are concise and can enable efficient computation for solving various problems related to cooperative game theory. Reformulation of Cooperative Game Theory based on Concise Representation Scheme Suguru Uedahas 3 papers
An Embodied Conversational Agent for Social Support Janneke van der Zwaanhas 2 papers
Stackelberg games have garnered signicant attention in recent years given their deployment for real world security, such as ARMOR, IRIS and GUARDS. Most of these systems have adopted the standard game-theoretical assumption that adversaries are perfectly rational, which may not hold in real-world security problems due to the bounded rationality of human adversaries and could potentially reduce the eectiveness of these systems. My thesis focuses on relaxing the assumption of perfectly rational adversaries in Stackelberg security games. In particular, I aim at developing new adversary models incorporating their bounded rationality and building new algorithms for eciently computing a defender's best response against these new models. To that end, I have developed a new adversary model using quantal response (QR) and a new ecient algorithm (Pasaq) to compute a defender's strategy against such a model in massive real-world security games. Experimental results with human subjects show that this new model gives signicantly better defender strategies than the previous leading contender. Furthermore, Pasaq has been deployed in a real-world security application, PROTECT, by the U.S. Coast Guards at the port of Boston. Recently, I started extending the model to incorporate features from more complicated games, including Network Security games and Bayesian Stackelberg games. Designing Better Resource Allocation Strategy against Human Adversaries in Security Games Rong Yanghas 5 papers
There has been significant recent research interest in utilizing leader-follower Stackelberg game in security applications. Indeed, Stackelberg games are seen at many deployed applications: ARMOR at Los Angeles International Airport, IRIS for Federal Air Marshals Service, GUARDS for the Transportation Security Administration, and TRUSTS for the Los Angeles Metro Rail System (under evaluation). The foundational assumption for using Stackelberg games is that security forces (leaders), acting first, commit to a randomized strategy; while their adversaries (followers) choose their best response after surveillance of this randomized strategy. Due to the adversarial environment and the nature of law enforcement activities, many types of uncertainty, such as execution, observation, and preference uncertainty, must be taken into account in game-theoretic modeling for practical security applications. To that end, focusing on security games I explicitly model the aforementioned uncertainty and present theoretical analysis and novel algorithms for computing robust solutions. Furthermore, as the cornerstone in providing real world evaluations of my robust solution techniques, I propose TRUSTS, a compact game-theoretic formulation, for fare evasion deterrence in the Los Angeles Metro Rail system. In my future research, I will extend TRUSTS to address real world uncertainty and evaluate the solutions within the LA Metro system. Addressing Uncertainty in Stackelberg Games for Security: Models and Algorithms Zhengyu Yinhas 4 papers
Stability and Arbitration in Cooperative Games with Overlapping Coalitions Yair Zickhas 2 papers
