Decentralized negotiation: An approach to the distributed planning problem

DENEGOT is a distributed planning framework that bases conflict resolution on decentralized peer negotiation. Negotiation is viewed as a distributed search through potential compromises. The framework assumes that a satisficing solution is acceptable (a reasonable assumption in many complex domains). To estimate the quality of potential solutions, the negotiation search space is structured into a lattice of sets of potential compromise solutions based on hard constraints. A solution in a higher set in the lattice, if it is achievable, will be preferable over a solution in a lower set. Agents first search under the hard constraint level representing the highest quality solution standard achievable in the current situation. By relaxing hard constraints, the set of compromises that qualify as a solution are enlarged. Agents search for a resolution under the relaxed hard constraint set when a solution cannot be found under the current set of constraints. The framework consists of three iterative problem-solving phases: coordinated search, negotiation state analysis, and constraint relaxation. The application of the DENEGOT framework to distributed planning problems in two domains is demonstrated.This work was partly supported by the Defense Advanced Research Projects Agency (DARPA), monitored by the Office of Naval Research under contract N00014-89-J-1877, by the Office of Naval Research under a University Research Initiative grant, number N00014-86-K-0764, and by the PRC Independent Research and Development project, number R00911. DPS.     

Search reduction in hierarchical distributed problem solving

Knoblock and Korf have determined that abstraction can reduce search at a single agent from exponential to linear complexity (Knoblock 1991; Korf 1987). We extend their results by showing how concurrent problem solving among multiple agents using abstraction can further reduce search to logarithmic complexity. We empirically validate our formal analysis by showing that it correctly predicts performance for the Towers of Hanoi problem (which meets all of the assumptions of the analysis). Furthermore, a powerful form of abstraction for large multiagent systems is to group agents into teams, and teams of agents into larger teams, to form an organizational pyramid. We apply our analysis to such an organization of agents and demonstrate the results in a delivery task domain. Our predictions about abstraction's benefits can also be met in this more realistic domain, even though assumptions made in our analysis are violated. Our analytical results thus hold the promise for explaining in general terms many experimental observations made in specific distributed AI systems, and we demonstrate this ability with examples from prior research.This research has been sponsored, in part, by the National Science Foundation under grants IRI-9015423 and IRI-9010645, by the University of Michigan Rackham Graduate School, and by a Bell Northern Research Postgraduate Award.     

Cooperative communication: Computerware and humanware

This article emphasizes the combined requirements of computer systems and humanistics. In cooperative computing, negotiations can be used as a basic paradigm by which different roles and their requisites can be identified—the facilitator, the mediator, and the negotiator. The negotiation‐cooperation process has a logical sequence of agreements, definition of terms, objectives, mode of operation, common security measures concerning integrity and liability, handling protocols, etc. The cooperation is based on models of the subject and the partners—i.e., a minimum of three models should be matched. The usual methods of human negotiations supported by metacommunication should have a computer‐realizable substitute. All these subjects are outgrowths of recent research in artificial intelligence (knowledge‐based systems) and cognitive psychology; some experiences are reported in the field. However, the main task is human‐oriented—education of people for this new powerful means of coexistence.     

Collaboration,motivation, and the size of organizations

We present an analytical model of an organization that offers operational drivers of limits on team size. The model trades off benefits from collaborative problem solving against the disadvantages of diminishing motivation when groups get large. Collaboration is represented as parallel employee activity combined with frequent sharing of partial information, with a resulting superlinear performance increase over team size. Motivation is modeled by team members periodically setting an effort level either to contribute to the best of their ability or to “cruise”; at the minimum level not recognizable as shirking. Each individual decision is limited by bounded rationality based on team rewards, the time horizon of team interaction, and individual expectations about colleagues’ behavior. The decision collapses to a simple “barrier rule”;. Work hard when a certain “barrier percentage”; of team members work hard, and otherwise shirk. The influence of team size on this barrier percentage depends on the extent of benefits from collaboration: As long as performance increases quadratically with team size, the increased benefits resulting from collaboration exactly balance the temptation to shirk, with the barrier percentage approaching a fixed limit for large team sizes. As soon as the performance increase slows to anything less than quadratic, shirking eventually sets in and limits the possible size of the team. This implies that cooperation is sustainable in large organizational units, provided the problem‐solving processes used are powerful enough to ensure sufficient performance increases. Thus, effective problem‐solving methods are of double value, improving direct productivity and mitigating the social dilemma from team production. A manager should enlarge his or her organization up to the minimum of the limit set by the cooperation barrier and the exogenous performance limit.     

Toward a theory of honesty and trust among communicating autonomous agents

This article outlines, through a number of examples, a method that can be used by autonomous agents to decide among potential messages to send to other agents, without having to assume that a message must be truthful and that it must be believed by the hearer. The main idea is that communicative behavior of autonomous agents is guided by the principle of economic rationality, whereby agents transmit messages to increase the effectiveness of interaction measured by their expected utilities. We are using a recursive, decision-theoretic formalism that allows agents to model each other and to infer the impact of a message on its recipient. The recursion can be continued into deeper levels, and agents can model the recipient modeling the sender in an effort to assess the truthfulness of the received message. We show how our method often allows the agents to decide to communicate in spite of the possibility that the messages will not be believed. In certain situations, on the other hand, our method shows that the possibility of the hearer not believing what it hears makes communication useless. Our method thus provides the rudiments of a theory of how honesty and trust could emerge through rational, selfish behavior.This research was supported, in part, by the Department of Energy under contract DG-FG-86NE37969, and by the National Science Foundation under grant IRI-9015423.     

Reaching informed agreement in multispecialist cooperation

Very often, complex decisions must be made by a group of specialists rather than a single decision maker. To make an effective decision, the combination of the group's expertise must be brought to bear on the situation. Fusing expertise where individuals have very detailed knowledge in their own areas and much weaker understanding of others is characterized by many difficulties: (1) agents cannot communicate their expertise in an intelligible way to nonexperts because of differences in vocabulary and conceptual content; (2) the process allows for incorrect inferences; and (3) no one knows what anyone else needs to know. This impasse cannot be broken until shared mental models are developed to provide a level of agreement in evaluating alternatives needed to focus the activity of the group. This article presents a model of decision making by teams of specialists in which agents' evaluations confound expert and naive inferences in judging alternatives. A partitioning of agent knowledge into expert and naive models is proposed. The naive portion of agents' models provides both a common language and the inferential skeleton needed for the development of shared models. Communications are categorized into types of evaluation or justification based on their form and the entities they involve within the agent models. A process of model refinement is outlined, linking communications among agents to modifications of the naive/ shared portions of their models. The process of cooperative problem solving by a team of specialists is characterized as a search among alternatives in which model refinement continually alters the agents' evaluations, leading to progressively greater accuracy and more precisely directed search. The model is intended as a research tool for investigating multiagent problem solving among people and machines.     

Influence of Computer-Based Meeting Support on Process and Outcomes for a Divisional Coordinating Group

This research examines the introduction of computer-based group decision support systems (GDSS) to members of a division level coordinating group. Participants performed authentic problem formulation tasks which varied naturally in degree of structuredness, in two non-GDSS meetings then were provided GDSS for four additional meetings. It was proposed that the introduction of the GDSS and task structuredness would influence (1) group process in terms of the amount of divergent and convergent thinking communicated during meetings and (2) perceived outcomes regarding quality, satisfaction, understanding, confidence, and commitment to group positions. Results suggest that GDSS use affected both the total amount and pattern of group communication but not perceived outcomes. Structuredness of the task affected perceived outcomes but neither amount nor patterns of communication. Additional qualitative data regarding perceived of costs and benefits in using GDSS provide richer explanation for study findings and suggest further lines of inquiry.     

Pricing coordination of a dual-channel supply chain considering offline in-sale service

With the rapid development of e-commerce, the problem of pricing conflicts between online and offline channels has become increasingly prominent. And the in-sale service has become an important factor influencing consumers' purchase decisions. To study the impact of in-sale service, this paper establishes a dual-channel supply chain model considering offline in-sale service. Using Stackelberg's game theory and backward induction, it solves the optimal pricing of supply chain members and makes comparisons in both cooperative and non-cooperative situations. Finally, it coordinates the supply chain through a two-part tariff contract. The results show that (1) The optimal wholesale price and offline retail price are positively correlated with in-sale service quality. And the opposite of the optimal online direct selling price. (2) With the quality of in-sale service improving, the retailer's profit will increase and then decrease but the manufacturer's profit will always decrease under non-cooperation. The total profit of the supply chain will rise and then fall under cooperation. (3) The two-part tariff coordination maximizes profits with the manufacturer reducing the wholesale price and the retailer paying a transfer cost. (4) Cooperative decision is better than the non-cooperative decision in terms of the supply chain as a whole.     

基于物联网操作系统平台构建智慧管网的思路研究

随着机器学习、图像识别等技术的发展,国家越来越重视人工智能技术在各领域的应用推广。在国家整体发展战略部署政策的引导以及支持下,智慧管网、智慧城市、智慧工业以及智能电网等概念逐渐被提出。文章首先对智慧管网的发展以及政策进行了梳理分析;然后对智慧管网方面的国内外发展现状进行了梳理,随后对物联网技术架构进行了分析;在此基础上,引入物联网操作系统平台的概念,提出一种基于物联网操作系统平台构建智慧管网的思路;最后对智慧管网建设进行了总结与展望。     

The impact of reverse logistics process coordination on third party relationship quality: A moderated mediation model for multichannel retailers in the fashion industry

Multichannel retailing is a widely adopted strategy in the fashion industry. Companies in this industry find it a source of competitive advantage to invest in reverse logistics infrastructure. However, limited empirical studies investigate the enablers of the relationship quality between the retailers and their reverse logistics service providers. This study quantifies the impact of reverse logistics process coordination between retailers and logistics service providers on relationship quality. Moreover, it tests the mediating role of reverse logistics service quality and the moderating role of conflict frequency in this relationship. Data were collected through a survey using a purposive sample of 241 retail store managers from the fashion retail industry of Pakistan. For this purpose, a self-administered questionnaire was developed using a five-point Likert Scale to gauge the responses. Conditional process analysis was used to evaluate the moderated mediation model. The findings showed a significant positive impact of reverse logistics process coordination on the relationship quality with a logistics service provider, a significant positive mediation effect of reverse logistics service quality, and a significant moderation effect of conflict frequency on the indirect relationship. However, conflict frequency, contrary to the hypothesis in the study, strengthened the indirect relationship. Furthermore, the moderation effect of conflict frequency on the direct relationship was insignificant. This study will help managers better understand the best practices leading to effective management of reverse logistics processes, particularly product returns.     

Brace yourself! Why managers should adopt a synthetic media incident response playbook in an age of falsity and synthetic media

Synthetic media presents looming threats to managers in a business setting. To address this issue, we first offer a short overview of the evolution of media manipulation to contextualize the new era of synthetic media. Then, we present the problems associated with synthetic media via veridicality and heuristics to illustrate how consumers have little choice but to believe what they see, read, and hear online. We outline the most likely and impactful types of synthetic media threats and attacks and present a synthetic media incident response playbook. Our aim is to inform managers about six specific phases so they can prepare, assess, detect, analyze, and recover from synthetic media incidents and coordinate their lessons learned.     

Siri,Alexa, and other digital assistants: a study of customer satisfaction with artificial intelligence applications

ABSTRACT

Digital assistants (e.g., Apple’s Siri, Amazon’s Alexa, Google’s Google Assistant) are highly complex and advanced artificial intelligence (AI) based technologies. Individuals can use digital assistants to perform basic personal tasks as well as for more advanced capabilities. Yet, the functional and topical use of a digital assistant tends to vary by individual. This study reflects the contextual experiences of the respondents. At present, there is little empirical evidence of customer satisfaction with digital assistants. PLS-SEM was used to analyse 244 survey responses to examine this research gap. The results confirmed that expectations and confirmation of expectations have a positive and significant relationship on customer satisfaction with digital assistants. This study provides evidence that customer expectations are being satisfied through the digital assistant interaction experience. As firms integrate digital assistants into their operations, they must help customers properly define what to expect from the firm’s interactive experience.     

Exploring the knowledge spillovers of a technology in an entrepreneurial ecosystem—The case of artificial intelligence in Sydney

New knowledge presents opportunities for commercial value and can hence be a critical asset for entrepreneurial ecosystems (EEs). In particular, general purpose technologies are major drivers of entrepreneurship. Thus, a nuanced understanding on technological knowledge and its spillovers among actors within an EE is warranted. Using knowledge‐spillover‐based strategic entrepreneurship theory, we propose to observe knowledge spillovers through the assessment of the knowledge bases of a technology in an EE. To do so, this article proposes to use three key sources of knowledge: publications reflecting the emerging knowledge base, patents representing the realized knowledge base, and startups showing the experimental knowledge base. This article uses secondary data sources such as Web of Science and applies the method of bibliometrics to illustrate how an assessment is carried out in practice by evaluating the artificial intelligence (AI) knowledge bases in Sydney from 2000 to 2018. The findings are summarized with an illustration of the evolution of the key actors and their activities over time in order to indicate the key strengths and weaknesses in Sydney's AI knowledge among the different bases. Contrary to expectations from the high potential of knowledge spillovers from a general purpose digital technology such as AI, the article shows that apparent knowledge spillovers are yet highly limited in Sydney. Even though Sydney has a strong emerging knowledge base, the realized knowledge base seems weak and the experimental knowledge base is slowly improving. That observation itself verifies the need to take strategic actions to facilitate knowledge spillovers within EEs. After the implications for theory and policy makers are discussed, suggestions for further studies are proposed.     

Qualitative representations of negotiations: Tutorial on a rule-based approach

This article introduces a logic-based approach for structuring and representing negotiation problems and for supporting negotiators. It is argued that rule-based formalism allows for integrating decision-making aspects unique and specific to negotiations with general reasoning mechanisms based on rationality postulates. The discussion of the rule-based systems and its application to negotiation modeling and support is preceded by an outline of qualitative and quantitative approaches to problem representation and reasoning, and an overview of predicate calculus. The advantages and disadvantages of rule-based systems and their ability to capture complex negotiation decision processes and reasoning are also given.     

Modeling and supporting task-oriented group processes: Purposeful complex adaptive systems and Evolutionary Systems Design

Evolutionary Systems Design (ESD) is a universal general problem solving, formal modeling, design framework for purposeful complex adaptive systems (PCAS) and processes, i.e., task-oriented group processes. These processes constitute policy making, group decision, negotiation, and multiagent problem solving with human and/or artificial agents. ESD is also a framework for computer group support systems (GSS) that support these processes. The ESD general framework can be applied to define and solve specific problems. In this article the ESD framework is presented and illustrated by example. The article provides background for ESD computer implementations discussed in two other related articles (Lewis and Shakun 1996; Bui and Shakun 1996).