Showing papers by "Munindar P. Singh published in 2018"

Blockchains for Business Process Management - Challenges and Opportunities

Abstract: Blockchain technology offers a sizable promise to rethink the way interorganizational business processes are managed because of its potential to realize execution without a central party serving as a single point of trust (and failure). To stimulate research on this promise and the limits thereof, in this article, we outline the challenges and opportunities of blockchain for business process management (BPM). We first reflect how blockchains could be used in the context of the established BPM lifecycle and second how they might become relevant beyond. We conclude our discourse with a summary of seven research directions for investigating the application of blockchain technology in the context of BPM.

Sociotechnical Systems and Ethics in the Large

Abstract: Advances in AI techniques and computing platforms have triggered a lively and expanding discourse on ethical decision making by autonomous agents. Much recent work in AI concentrates on the challenges of moral decision making from a decision-theoretic perspective, and especially the representation of various ethical dilemmas. Such approaches may be useful but in general are not productive because moral decision making is as context-driven as other forms of decision making, if not more. In contrast, we consider ethics not from the standpoint of an individual agent but of the wider sociotechnical systems (STS) in which the agent operates. Our contribution in this paper is the conception of ethical STS founded on governance that takes into account stakeholder values, normative constraints on agents, and outcomes (states of the STS) that obtain due to actions taken by agents. An important element of our conception is accountability, which is necessary for adequate consideration of outcomes that prima facie appear ethical or unethical. Focusing on STS provides a basis for tackling the difficult problems of ethics because the norms of an STS give an operational basis for agent decision making.

Robust Norm Emergence by Revealing and Reasoning about Context: Socially Intelligent Agents for Enhancing Privacy

Abstract: Norms describe the social architecture of a society and govern the interactions of its member agents. It may be appropriate for an agent to deviate from a norm; the deviation being indicative of a specialized norm applying under a specific context. Existing approaches for norm emergence assume simplified interactions wherein deviations are negatively sanctioned. We investigate via simulation the benefits of enriched interactions where deviating agents share selected elements of their contexts. We find that as a result (1) the norms are learned better with fewer sanctions, indicating improved social cohesion; and (2) the agents are better able to satisfy their individual goals. These results are robust under societies of varying sizes and characteristics reflecting pragmatic, considerate, and selfish agents.

A Conceptual Framework for Engineering Chatbots

Abstract: The increasing popularity of chatbots as virtual assistants has lead to many organizations releasing If-This-Then-That frameworks to engineer such chatbots. However, these frameworks often result in inflexible and difficult-to-maintain chatbots. This paper outlines a high-level conceptual framework for realizing flexible chatbots founded upon agent-oriented abstractions: goals, plans, and commitments.

Designing Ethical Personal Agents

Abstract: The authors consider the problem of engineering ethical personal agents. Such an agent would understand the applicable social norms and its users preferences among values. It would act or recommend actions that promote preferred values, especially, in scenarios where the norms conflict.

Bit-Map-Assisted Energy-Efficient MAC protocol for Wireless Sensor Networks

Abstract: Time Division Multiple Access (TDMA) based Medium Access Control (MAC) protocols allocate one or more time slots to each sensor node. A node transmits its data during these slot(s) and keeps its radio OFF during all other slots. This inherently reduces idle listening, overhearing, and collision which are the major energy consuming factors. Some MAC protocols include a control period within each time frame. A node that has data for its Cluster Head (CH), books one or more slots by sending a data request (inside a control packet) during the control period. In response to the requests received, the CH sets up and broadcasts a schedule for the source nodes. Each source node transmits in its own slot(s) and keeps its radio OFF during the rest of the data transmission period. In the proposed MAC protocol named Bit-Map-Assisted Energy-Efficient MAC (BEE-MAC), the schedule is broadcasted only once in a round, and is followed throughout the duration of the round. This saves the energy which would have been consumed otherwise, in sending and receiving schedules, multiple times in a single round. The energy consumption in BEE-MAC is compared with the energy consumption in an existing MAC protocol. The results, which have been derived through mathematical calculations, show that BEE-MAC can save a considerable amount of energy by allowing the CH to broadcast the schedule only once in a round.

Limbic: Author-Based Sentiment Aspect Modeling Regularized with Word Embeddings and Discourse Relations.

Abstract: We propose Limbic, an unsupervised probabilistic model that addresses the problem of discovering aspects and sentiments and associating them with authors of opinionated texts. Limbic combines three ideas, incorporating authors, discourse relations, and word embeddings. For discourse relations, Limbic adopts a generative process regularized by a Markov Random Field. To promote words with high semantic similarity into the same topic, Limbic captures semantic regularities from word embeddings via a generalized Polya Urn process. We demonstrate that Limbic (1) discovers aspects associated with sentiments with high lexical diversity; (2) outperforms state-of-the-art models by a substantial margin in topic cohesion and sentiment classification.

GoCo: planning expressive commitment protocols

Abstract: This article addresses the challenge of planning coordinated activities for a set of autonomous agents, who coordinate according to social commitments among themselves. We develop a multi-agent plan in the form of a commitment protocol that allows the agents to coordinate in a flexible manner, retaining their autonomy in terms of the goals they adopt so long as their actions adhere to the commitments they have made. We consider an expressive first-order setting with probabilistic uncertainty over action outcomes. We contribute the first practical means to derive protocol enactments which maximise expected utility from the point of view of one agent. Our work makes two main contributions. First, we show how Hierarchical Task Network planning can be used to enact a previous semantics for commitment and goal alignment, and we extend that semantics in order to enact first-order commitment protocols. Second, supposing a cooperative setting, we introduce uncertainty in order to capture the reality that an agent does not know for certain that its partners will successfully act on their part of the commitment protocol. Altogether, we employ hierarchical planning techniques to check whether a commitment protocol can be enacted efficiently, and generate protocol enactments under a variety of conditions. The resulting protocol enactments can be optimised either for the expected reward or the probability of a successful execution of the protocol. We illustrate our approach on a real-world healthcare scenario.

Violable Contracts and Governance for Blockchain Applications.

Abstract: We examine blockchain technologies, especially smart contracts, as a platform for decentralized applications. By providing a basis for consensus, blockchain promises to upend business models that presuppose a central authority. However, blockchain suffers from major shortcomings arising from an over-regimented way of organizing computation that limits its prospects. We propose a sociotechnical, yet computational, perspective that avoids those shortcomings. A centerpiece of our vision is the notion of a declarative, violable contract in contradistinction to smart contracts. This new way of thinking enables flexible governance, by formalizing organizational structures; verification of correctness without obstructing autonomy; and a meaningful basis for trust.

Compositional Correctness in Multiagent Interactions

Abstract: An interaction protocol specifies the constraints on communication between agents in a multiagent system. Ideally, we would like to be able to treat protocols as modules and compose them in a declarative manner to systematically build more complex protocols. Supporting composition correctly requires taking into account information-based causality relationships between protocols. One important problem that may arise from inadequate consideration of such relationships is that the enactment of a composite protocol may violate atomicity ; that is, some components may be initiated but prevented from completing. We use the well-known all or nothing principle as the basis for formalizing atomicity as a novel correctness property for protocols. Our contributions are the following. One, we motivate and formalize atomicity and highlight its distinctiveness from related correctness notions. Two, we give a decision procedure for verifying atomicity and report results from an implementation. For concreteness of exposition and technical development, we adopt BSPL as an exemplar of information causality approaches.

Teaching Crowdsourcing: An Experience Report

Abstract: Crowdsourcing is the process of accomplishing a task by using a typically open call to invite members of the public (the “crowd”) to work on one's task. The authors describe a project assignment in which students received the opportunity of practicing crowdsourcing to accomplish a hummed song recognition task, yielding improved comprehension of the concept and high student satisfaction.

Toward extraction of security requirements from text: poster

Abstract: We propose and evaluate an information extraction and analysis framework that combines human intelligent (crowdsourcing) with automated methods to produce improved security and privacy requirements incorporating knowledge from post-deployment artifacts such as breach reports.

The Causal Link between News Framing and Legislation.

Abstract: We demonstrate that framing, a subjective aspect of news, is a causal precursor to both significant public perception changes, and to federal legislation. We posit, counter-intuitively, that topic news volume and mean article similarity increase and decrease together. We show that specific features of news, such as publishing volume , are predictive of both sustained public attention, measured by annual Google trend data, and federal legislation. We observe that public attention changes are driven primarily by periods of high news volume and mean similarity, which we call \emph{prenatal periods}. Finally, we demonstrate that framing during prenatal periods may be characterized by high-utility news \emph{keywords}.

Analysing Tuberculosis Trends in South Asia

Abstract: Tuberculosis (TB) has been one of the top ten causes of death in the world. As per the World Health Organization (WHO) around 1.8 million people have died due to tuberculosis in 2015. This paper aims to investigate the spatial and temporal variations in TB incident in South Asia (India, Bangladesh, Pakistan, Maldives, Nepal, and Sri-Lanka). Asia had been counted for the largest number of new TB cases in 2015. The paper underlines and relates the relationship between various features like gender, age, location, occurrence, and mortality due to TB in these countries for the period 1993-2012.

Medical Alert System Using Social Data

Abstract: Medical data research and analysis is a heuristic-based system that analyzes disease in metropolitan cities of India like Delhi using input from the cyber social system. This provides information regarding the level of infections in different localities and correlations among events which cause that disease. The inputs from physical cyber social systems are analyzed according to different locations of the city listed in another record. From the data collected, crucial information pertaining to studying infected patterns was extracted. Obtained with the results, we analyze the data from social media and generate a heuristic-based opinion.

Framing Matters: Predicting Framing Changes and Legislation from Topic News Patterns.

Abstract: News has traditionally been well researched, with studies ranging from sentiment analysis to event detection and topic tracking We extend the focus to two surprisingly under-researched aspects of news: \emph{framing} and \emph{predictive utility} We demonstrate that framing influences public opinion and behavior, and present a simple entropic algorithm to characterize and detect framing changes We introduce a dataset of news topics with framing changes, harvested from manual surveys in previous research Our approach achieves an F-measure of $F_1=096$ on our data, whereas dynamic topic modeling returns $F_1=01$ We also establish that news has \emph{predictive utility}, by showing that legislation in topics of current interest can be foreshadowed and predicted from news patterns

Ethics, values, and personal agents: poster

Abstract: We address the problem of designing privacy-preserving ethical personal agents that understand and act according to their users' preferred values and ethical principles, and provide a satisfying social experience to all their stakeholders.

Extraction of Natural Language Requirements from Breach Reports Using Event Inference

Abstract: We address the problem of extracting useful information contained in security and privacy breach reports. A breach report tells a short story describing how a breach happened and the follow-up remedial actions taken by the responsible parties. By predicting sentences that may follow a breach description using natural language processing, our goal is to suggest security and privacy requirements for practitioners and end users that can be used to prevent and recover from such breaches. We prepare a curated dataset of structured short breach stories using unstructured breach reports published by the U.S. Department of Health and Human Services. We propose a prediction model for inferring held-out sentences based on Paragraph Vector, a document embedding method, and Long Short-Term Memory networks. The predicted sentences can suggest natural language requirements. We evaluate our model on the curated dataset as well as the ROCStories corpus, a collection of five-sentence commonsense stories, and find that the presented model performs significantly better than the baseline of using average word vectors.