Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

Allocating educational resources through happiness maximization and traditional CSP approach

This is an electronic version of the paper presented at the 4th International Conference on Software and Data Technologies, held in Sofia on 2009An instance of an Educational Resources Allocation (ERA) problem is the distribution of a set of students in different laboratories. This can be a complex and dynamic problem if non-quantitative considerations (i.e. how close the final allocation is to the student preferences or desires) are involved in the decision process. Traditionally, different approaches based on Constraint-Satisfaction techniques and Multi-agent negotiation have been applied to the general problem of Resource Allocation. This paper shows how a Multi-agent approach can be used to model and simulate the assignment of sets of students to several predefined laboratories, by using their preferences to guide the allocation process. This approach aims at finding new solutions that try to satisfy individual student needs with no knowledge about the general allocation problem. The paper shows some experimental results and a comparison, between a CSP-based solution modeled in CHOCO, a CSP Java-based library, and a Multi-agent model implemented using MASON, a multi-agent simulation platform.This work has been supported by research projects TIN2007-65989 and TIN2007-64718. We also thank IBM for its support to the Linux Reference Cente

Environmental Quality Laboratory Research Report, 1985-1987

The Environmental Quality Laboratory at Caltech is a center for research on large-scale systems problems of natural resources and environmental quality. The principal areas of investigation at EQL are: 1. Air quality management. 2. Water resources and water quality management. 3. Control of hazardous substances in the environment. 4. Energy policy, including regulation, conservation and energy-environment tradeoffs. 5. Resources policy (other than energy); residuals management. EQL research includes technical assessments, computer modeling, studies of environmental control options, policy analyses, and research on important components of the large-scale systems. Field work is also undertaken at EQL, some in collaboration with other organizations, to provide critical data needed for evaluation of systems concepts and models. EQL's objectives are as follows: 1. To do systematic studies of environmental and resources problems. The results of these studies, including the clarification of policy alternatives, are communicated to decision-makers in government and industry, to the research community, and to the public. As an organization, EQL refrains from advocating particular policies, but seeks to point out the implications of the various policy alternatives. 2. To contribute to the education and training of people in these areas through involvement of predoctoral students, postdoctoral fellows, and visiting faculty members in EQL activities. This educational effort is just as important as the results of the studies themselves, and should make lasting contributions to the nation's ability to solve its environmental and resources problems. The work at EQL goes beyond the usual academic research in that it tries to organize and develop the knowledge necessary to clarify society's alternatives by integrating relevant disciplines. EQL works on solving problems of specific localities when there is a strong element of public interest or educational value, or the concepts and results are applicable to other places. The research of EQL during this period was done under the supervision of faculty members in Environmental Engineering Science, Chemical Engineering, and Social Science. This research report covers the period from October 1985 through September 1987. The publications listed under the individual project descriptions are the new ones for the reporting period

Modeling social norms in real-world agent-based simulations

Studying and simulating social systems including human groups and societies can be a complex problem. In order to build a model that simulates humans\u27 actions, it is necessary to consider the major factors that affect human behavior. Norms are one of these factors: social norms are the customary rules that govern behavior in groups and societies. Norms are everywhere around us, from the way people handshake or bow to the clothes they wear. They play a large role in determining our behaviors. Studies on norms are much older than the age of computer science, since normative studies have been a classic topic in sociology, psychology, philosophy and law. Various theories have been put forth about the functioning of social norms. Although an extensive amount of research on norms has been performed during the recent years, there remains a significant gap between current models and models that can explain real-world normative behaviors. Most of the existing work on norms focuses on abstract applications, and very few realistic normative simulations of human societies can be found. The contributions of this dissertation include the following: 1) a new hybrid technique based on agent-based modeling and Markov Chain Monte Carlo is introduced. This method is used to prepare a smoking case study for applying normative models. 2) This hybrid technique is described using category theory, which is a mathematical theory focusing on relations rather than objects. 3) The relationship between norm emergence in social networks and the theory of tipping points is studied. 4) A new lightweight normative architecture for studying smoking cessation trends is introduced. This architecture is then extended to a more general normative framework that can be used to model real-world normative behaviors. The final normative architecture considers cognitive and social aspects of norm formation in human societies. Normative architectures based on only one of these two aspects exist in the literature, but a normative architecture that effectively includes both of these two is missing

A Survey on Large Language Model based Autonomous Agents

Autonomous agents have long been a prominent research topic in the academic community. Previous research in this field often focuses on training agents with limited knowledge within isolated environments, which diverges significantly from the human learning processes, and thus makes the agents hard to achieve human-like decisions. Recently, through the acquisition of vast amounts of web knowledge, large language models (LLMs) have demonstrated remarkable potential in achieving human-level intelligence. This has sparked an upsurge in studies investigating autonomous agents based on LLMs. To harness the full potential of LLMs, researchers have devised diverse agent architectures tailored to different applications. In this paper, we present a comprehensive survey of these studies, delivering a systematic review of the field of autonomous agents from a holistic perspective. More specifically, our focus lies in the construction of LLM-based agents, for which we propose a unified framework that encompasses a majority of the previous work. Additionally, we provide a summary of the various applications of LLM-based AI agents in the domains of social science, natural science, and engineering. Lastly, we discuss the commonly employed evaluation strategies for LLM-based AI agents. Based on the previous studies, we also present several challenges and future directions in this field. To keep track of this field and continuously update our survey, we maintain a repository for the related references at https://github.com/Paitesanshi/LLM-Agent-Survey.Comment: 32 pages, 3 figure

Computational Explorations of Information and Mechanism Design in Markets

Markets or platforms assemble multiple selfishly-motivated and strategic agents. The outcomes of such agent interactions depend heavily on the rules, regulations, and norms of the platform, as well as the information available to agents. This thesis investigates the design and analysis of mechanisms and information structures through the ``computational lens\u27\u27 in both static and dynamic settings. It both addresses the outcome of single platforms and fills a gap in the study of the dynamics of multiple platform interactions. In static market settings, we are particularly interested in the role of information, because mechanisms are harder to change than the information available to participants. We approach information design through specific examples, i.e., matching markets and auction markets. First, in matching markets, we study the situation where the matching is preceded by a costly interviewing stage in which firms acquire information about the qualities of candidates. We focus on the impact of the signals of quality available prior to the interviewing stage. We show that more ``commonality\u27\u27 in the quality of information can be harmful, yielding fewer matches. Second, in auction markets, we design an information environment for revenue enhancement in a sealed-bid second price auction. Much of the previous literature has focused on signal design in settings where bidders are symmetrically informed, or on the design of optimal mechanisms under fixed information structures. Here, we provide new theoretical insights for complex situations like corporate mergers, where the sender of the signal has the opportunity to communicate in different ways to different receivers. Next, in dynamic markets, we focus on two dimensions: (1) the effects of different market-clearing rules on market outcomes and (2) the dynamics of multiple platform interactions. Considering both dimensions, we investigate two important real-world dynamic markets: kidney exchange and financial markets. Specifically, in kidney exchange, we analyze the performance of different market-clearing algorithms and design a competing-market model to quantify the social welfare loss caused by market competition and exchange fragmentation. Here, we present the first analysis of equilibrium behavior in these dynamic competing matching market systems, from the viewpoints of both agents and markets. To improve the performance of kidney exchange in terms of both social welfare and individual utility, we analyze the benefit of convincing directed donation pairs to participate in paired kidney exchange, measured in terms of long-term graft survival. We provide the first empirical evidence that including compatible pairs dramatically benefits both social welfare and individual outcomes. For financial markets, in the debate over high frequency trading, the frequent call (Call) mechanism has recently received considerable attention as a proposal for replacing the continuous double auction (CDA) mechanisms that currently run most financial markets. We examine agents\u27 profit under CDA and frequent call auctions in a dynamic environment. We design an agent-based model to study the competition between these two market policies and show that CALL markets can drive trade away from CDAs. The results help to inform this very important debate