Showing papers in "ACM Transactions on Intelligent Systems and Technology in 2010"

Social media as crisis platform: The future of community maps/crisis maps

Abstract: Social media provides the means for creating new communities and for reenergizing old communities. Recently, a new kind of quickly formulated, powerful community has formed as existing social media communities, news organizations, and users have converged in social media spaces to respond to sudden tragedies. This article addresses the ad-hoc crisis community, whith uses the social madia as a crisis platform to generate community crisis maps.

Applying planning to interactive storytelling: Narrative control using state constraints

Abstract: We have seen ten years of the application of AI planning to the problem of narrative generation in Interactive Storytelling (IS). In that time planning has emerged as the dominant technology and has featured in a number of prototype systems. Nevertheless key issues remain, such as how best to control the shape of the narrative that is generated (e.g., by using narrative control knowledge, i.e., knowledge about narrative features that enhance user experience) and also how best to provide support for real-time interactive performance in order to scale up to more realistic sized systems. Recent progress in planning technology has opened up new avenues for IS and we have developed a novel approach to narrative generation that builds on this. Our approach is to specify narrative control knowledge for a given story world using state trajectory constraints and then to treat these state constraints as landmarks and to use them to decompose narrative generation in order to address scalability issues and the goal of real-time performance in larger story domains. This approach to narrative generation is fully implemented in an interactive narrative based on the “Merchant of Venice.” The contribution of the work lies both in our novel use of state constraints to specify narrative control knowledge for interactive storytelling and also our development of an approach to narrative generation that exploits such constraints. In the article we show how the use of state constraints can provide a unified perspective on important problems faced in IS.

Planning for human-robot teaming in open worlds

Abstract: As the number of applications for human-robot teaming continue to rise, there is an increasing need for planning technologies that can guide robots in such teaming scenarios. In this article, we focus on adapting planning technology to Urban Search And Rescue (USAR) with a human-robot team. We start by showing that several aspects of state-of-the-art planning technology, including temporal planning, partial satisfaction planning, and replanning, can be gainfully adapted to this scenario. We then note that human-robot teaming also throws up an additional critical challenge, namely, enabling existing planners, which work under closed-world assumptions, to cope with the open worlds that are characteristic of teaming problems such as USAR. In response, we discuss the notion of conditional goals, and describe how we represent and handle a specific class of them called open world quantified goals. Finally, we describe how the planner, and its open world extensions, are integrated into a robot control architecture, and provide an empirical evaluation over USAR experimental runs to establish the effectiveness of the planning components.

Human-aware task planning: An application to mobile robots

Abstract: Consider a house cleaning robot planning its activities for the day. Assume that the robot expects the human inhabitant to first dress, then have breakfast, and finally go out. Then, it should plan not to clean the bedroom while the human is dressing, and to clean the kitchen after the human has had breakfast. In general, robots operating in inhabited environments, like households and future factory floors, should plan their behavior taking into account the actions that will be performed by the humans sharing the same environment. This would improve human-robot cohabitation, for example, by avoiding undesired situations for the human. Unfortunately, current task planners only consider the robot's actions and unexpected external events in the planning process, and cannot accommodate expectations about the actions of the humans.In this article, we present a human-aware planner able to address this problem. Our planner supports alternative hypotheses of the human plan, temporal duration for the actions of both the robot and the human, constraints on the interaction between robot and human, partial goal achievement and, most importantly, the possibility to use observations of human actions in the policy generated for the robot. Our planner has been tested both as a stand-alone component and within a full framework for human-robot interaction in a real environment.

Opinion formation under costly expression

Abstract: Opinions play an important role in trust building and the creation of consensus about issues and products and a number of studies have focused on the design, evaluation, and utilization of online opinion systems However, little effort has been spent on the dynamic aspects of online opinion formation In this article, we study the dynamics of online opinion expression by analyzing the temporal evolution of vey large sets of user views and determine that in the course of time, later opinions tend to show a big difference with earlier opinions, which moderates the average opinion to the less extreme Online posters also tend to disagree with previous opinions when the cost of expression is high

Introduction to ACM TIST

Abstract: It is our great pleasure to present this inaugural issue of the ACM Transactions on Intelligent Systems and Technology (ACM TIST). In today’s world, systems empowered with artificial intelligence (AI) technology have truly touched on every aspect of our lives, ranging from Web search to smart phones, from social networking and media systems to computational sustainability. Looking back, the field of AI has undergone tremendous changes throughout the years since its inception in the 1950s. Today’s AI application has grown from the standalone, single systems in the early days to ones that are more pervasive, integrated, embedded, and multidisciplinary. AI systems are becoming more integrated, with more than one technology operating and interacting therein as well as becoming more embedded by acting as key components of a larger, overall system or systems. Increasingly, systems and technologies are data driven, which complements the traditional top-down design methodologies in AI. Intelligent systems are also stepping out of the traditional computer science realm as evidenced by explosive research in areas such as bioinformatics and biomedicine, intelligent education systems, and intelligent transportation systems. In light of the technology and societal changes just mentioned, we see a tremendous demand for opening up a new archival journal at a top venue to document high-impact works in the area of intelligent systems and technology. As we state in our editorial charter published at http://tist.acm.org, ACM Transactions on Intelligent Systems and Technology is a new scholarly journal that publishes the highest quality articles on intelligent systems, applicable algorithms, and technology with a multidisciplinary perspective. An intelligent system is one that uses artificial intelligence techniques to offer important services (e.g., as a component of a larger system) that allow integrated systems to perceive, reason, learn, and act intelligently in the real world. The journal welcomes articles that report on the integration of artificial intelligence technology with various subareas of computer science as well as with other branches of science and engineering. The journal welcomes innovative high-impact articles on deployed or emerging intelligent systems and technology with solid evaluation or evidence of success on a variety of topics. In a field where there are already many top conferences every year disseminating volumes of good papers, do we still need to have another journal? In his Communications of ACM article “Conferences vs. journals in computing research”, Moshe Y. Vardi [2009] ponders whether computer science as a field has now reached a maturity such that we should see more journal publications as a way to disseminate our results. He asks, “Why are we the only discipline driving on the conference side of the ‘publication road?”’ Many authors have had the experience with a typical computer science journal submission incurring much

A constraint-based approach to scheduling an individual's activities

Abstract: The goal of helping to automate the management of an individual's time is ambitious in terms both of knowledge engineering and of the quality of the plans produced by an AI system. Modeling an individual's activities is itself a challenge, due to the variety of activity, constraint, and preference types involved. Activities might be simple or interruptible; they might have fixed or variable durations, constraints over their temporal domains, and binary constraints between them. Activities might require the individual being at specific locations in order, whereas traveling time should be taken into account. Some activities might require exclusivity, whereas others can be overlapped with compatible concurrent activities. Finally, while scheduled activities generate utility for the individual, extra utility might result from the way activities are scheduled in time, individually and in conjunction.This article presents a rigorous, expressive model to represent an individual's activities, that is, activities whose scheduling is not contingent on any other person. Joint activities such as meetings are outside our remit; it is expected that these are arranged manually or through negotiation mechanisms and they are considered as fixed busy times in the individual's calendar. The model, formulated as a constraint optimization problem, is general enough to accommodate a variety of situations. We present a scheduler that operates on this rich model, based on the general squeaky wheel optimization framework and enhanced with domain-dependent heuristics and forward checking. Our empirical evaluation demonstrates both the efficiency and the effectiveness of the selected approach. Part of the work described has been implemented in the SelfPlanner system, a Web-based intelligent calendar application that utilizes Google Calendar.

Planning interventions in biological networks

Abstract: Modeling the dynamics of biological processes has recently become an important research topic in computational biology and systems engineering. One of the most important reasons to model a biological process is to enable high-throughput in-silico experiments that attempt to predict or intervene in the process. These experiments can help accelerate the design of therapies through their rapid and inexpensive replication and alteration. While some techniques exist for reasoning with biological processes, few take advantage of the flexible and scalable algorithms popular in AI research. In reasoning about interventions in biological processes, where scalability is crucial for feasible application, we apply AI planning-based search techniques and demonstrate their advantage over existing enumerative methods. We also present a novel formulation of intervention planning that relies on models that characterize and attempt to change the phenotype of a system. We study three biological systems: the yeast cell cycle, a model of the human aging process, and the Wnt5a network governing the metastasis of melanoma in humans. The contribution of our investigation is in demonstrating that: (i) prior approaches, based on dynamic programming, cannot scale as well as heuristic search, and (ii) the newly found scalability enables us to plan previously unknown sequences of interventions that reveal novel and biologically significant responses in the systems which are consistent with biological knowledge in the literature.

Evolution of state-dependent risk preferences

Abstract: Researchers have invested much effort in constructing models of the state-dependent (sometimes risk-averse and sometimes risk-prone) nature of human decision making. An important open question is how state-dependent risk behavior can arise and remain prominent in populations. We believe that one part of the answer is the interplay between risk-taking and sequentiality of choices in populations subject to evolutionary population dynamics. To support this hypothesis, we provide simulation and analytical results for evolutionary lottery games, including results on evolutionary stability. We consider a parameterized class of imitation dynamics in which the parameter 0 l α l 1 yields the replicator dynamic with α = 1 and the imitate-the-better dynamic with α = 0. Our results demonstrate that for every population dynamic in this class except for the replicator dynamic, the interplay between risk-taking and sequentiality of choices allows state-dependent risk behavior to have an evolutionary advantage over expected-value maximization.

Accessible image search for colorblindness

Abstract: This article introduces an intelligent system that accommodates colorblind users in image search. Color plays an important role in the human perception and recognition of images. However, there are about 8p of men and 0.8p of women suffering from colorblindness. We show that the existing image search techniques cannot provide satisfactory results for these users since many images will not be well perceived by them due to the loss of color information. To deal with this difficulty, we introduce a system named Accessible Image Search (AIS) to accommodate these users. Different from the general image search scheme that aims at returning more relevant results, AIS further takes into account the colorblind accessibilities of the returned results, that is, the image qualities in the eyes of colorblind users. The system contains three components: accessibility assessment, accessibility improvement, and color indication. The accessibility assessment component measures the accessibility scores of images, and consequently different reranking methods can be performed to prioritize images with high accessibilities. In the accessibility improvement component, we propose an efficient recoloring algorithm to modify the colors of the images such that they can be better perceived by colorblind users. Color indication aims to indicate the name of the interesting color in an image. We evaluate the introduced system with more than 60 queries and 20 anonymous colorblind users, and the empirical results demonstrate its effectiveness and usefulness.

Structured coalitions in resource selection games

Abstract: We study stability against coalitional deviations in resource selection games where the coalitions have a certain structure In particular, the agents are partitioned into coalitions, and only deviations by the prescribed coalitions are considered This is in contrast to the classical concept of strong equilibrium according to which any subset of the agents may deviate In resource selection games, each agent selects a resource from a set of resources, and its payoff is an increasing (or nondecreasing) function of the number of agents selecting its resource While it has been shown that a strong equilibrium always exists in resource selection games, a closer look reveals severe limitations to the applicability of the existence result even in the simplest case of two identical resources with increasing cost functions First, these games do not possess a super strong equilibrium in which a fruitful deviation benefits at least one deviator without hurting any other deviator Second, a strong equilibrium may not exist when the game is played repeatedly We prove that for any given partition, there exists a super strong equilibrium for resource selection games of identical resources with increasing cost functions In addition, we show similar existence results for a variety of other classes of resource selection games For the case of repeated games, we characterize partitions that guarantee the existence of a strong equilibrium Together, our work introduces a natural concept, which turns out to lead to positive and applicable results in one of the basic domains studied in the literature

Virtual worlds as cultural models

Abstract: Thirteen gamelike virtual worlds illustrate issues that overlap social science and information science, because they embody rather clear theories of society and culture: World of Warcraft, Lord of the Rings Online, Dark Age of Camelot, Age of Conan, Pirates of the Burning Sea, A Tale in the Desert, Entropia Universe, Anarchy Online, The Matrix Online, Tabula Rasa, EVE Online, Star Trek Online, and Dungeons and Dragons Online. A fourteenth, Star Wars Galaxies, illustrates the possibility that not all virtual worlds embody clear theories. After describing the thirteen, this essay discusses their economic systems, social systems, communication challenges, and the ways in which autonomous agents and semi-autonomous secondary avatars enrich interactive complexity.

CORALS: A real-time planner for anti-air defense operations

Abstract: Forces involved in modern conflicts may be exposed to a variety of threats, including coordinated raids of advanced ballistic and cruise missiles. To respond to these, a defending force will rely on a set of combat resources. Determining an efficient allocation and coordinated use of these resources, particularly in the case of multiple simultaneous attacks, is a very complex decision-making process in which a huge amount of data must be dealt with under uncertainty and time pressure. This article presents CORALS (COmbat Resource ALlocation Support), a real-time planner developed to support the command team of a naval force defending against multiple simultaneous threats. In response to such multiple threats, CORALS uses a local planner to generate a set of local plans, one for each threat considered apart, and then combines and coordinates them into a single optimized, conflict-free global plan. The coordination is performed through an iterative process of plan merging and conflict detection and resolution, which acts as a plan repair mechanism. Such an incremental plan repair approach also allows adapting previously generated plans to account for dynamic changes in the tactical situation.

Introduction to the ACM TIST special issue AI in social computing and cultural modeling

Abstract: Computer technology is leading to sweeping changes in the relationship between AI research and the social and behavioral sciences. Two closely related topics, social computing and cultural modeling, have become especially active and dynamic areas of research. Social computing is the study of social behavior and social context based on computational systems; cultural modeling is the modeling of perceptions and attitudes that are shared across social groups. The research in these two areas promises to provide a deeper understanding of behaviors, patterns, and potential outcomes. For this special issue, our aim was to provide a forum for interdisciplinary researchers to share their views and report original, cutting-edge research in social computing and culture modeling. Our call for papers elicited a tremendous response, and, after a careful review process by international experts, we have more high-quality submissions than will fit into a single issue of ACM TIST. Thus this special issue will be the first of a two-part series. Four of the five papers in this issue are theoretical and applied contributions that propose novel frameworks and processes for modeling social behaviors and cultures and investigate effective principles and approaches for acquiring, representing, modeling, and engaging ubiquitous intelligence in real-world problems. The fifth paper is a position article on future directions for social computing and cultural modeling. In “Virtual Worlds as Cultural Models,” William Bainbridge examines the relationships among online games and theories of society and culture. The work is based on more than 4,000 hours of participant observations involving 13 popular online games and discusses their economic systems, social systems, communication challenges, and the ways in which autonomous agents and semi-autonomous secondary avatars enrich interactive complexity. Resource selection games are an important game-theoretic model of situations in which agents need to choose among various resources, and in “Structured Coalitions in Resource Selection Games,” Michal Feldman and Moshe Tennenholtz consider a way to introduce social structure into such games. In particular, they partition the agents into coalitions and consider a strategy profile to be desirable to a coalition if it improves the payoffs of all of the agents in the coalition. They define game-theoretic equilibria relative to this definition of desirability and characterize the conditions under which such equilibria exist. In “Opinion Formation Under Costly Expression,” Fang Wu and Bernardo Huberman study the dynamics of public opinion formulation in online postings of recommendations and product reviews with special attention to the temporal dimension of opinion. The authors’ results show that in the process of