Explaining ourselves: human-aware constraint reasoning

Human-aware AI is increasingly important as AI becomes more powerful and ubiquitous. A good foundation for human-awareness should enable ourselves and our “AIs” to “explain ourselves” naturally to each other. Constraint reasoning offers particular opportunities and challenges in this regard. This paper takes note of the history of work in this area and encourages increased attention, laying out a rough research agenda

The Coalition Model, a Private-Public Strategic Innovation Policy Model For Encouraging Entrepreneurship and Economic Growth in the Era Of New Economic Challenges

Innovation driven entrepreneurial firms have an important role in contributing to job creation, generating technological innovation, and stimulating the United States economy. However, there is recently a notable decline in emerging growth entrepreneurial activity in the United States. The Coalition Model proposes ways to maximize opportunities for industry, academia, and government to collaborate and build sustainable relationships, to help convert the current challenges in the U.S. market into opportunities. Designing a new innovation strategy will lead the United States in generating innovation, technology, and economic growth, as well as help the federal government harness new approaches for institutional change. Adopting the Coalition Model (the Model) will not only bridge some of the financial inefficiencies and information gaps associated with investment in innovation driven enterprises, but, perhaps more importantly, will serve as a catalyst for encouraging and stimulating the development of new firms and technologies. The Model is built on the notion of taking a proactive approach to innovation. The model encourages government agencies to fund research and innovation, by identifying specific technological challenges, determining the course of the research that can benefit their needs, collaborating with audiences in the public sector, research institutions, and universities, and private corporations to act on these needs, and advancing commercialization efforts. There are several potential benefits to adopting such a proactive policy. First, it might encourage future engineers, scientists, and innovators to take a risk and become entrepreneurs. Second, it provides direct funding to research and development needs that might not otherwise be used. Third, it can signal that there are opportunities for private investors to invest in such ventures, and perhaps even serve as some sort of certification. Fourth, it will create a direct pathway for small firms to access government procurement. Fifth, it will encourage knowledge spillovers between professionals in government, industry, and academia. Finally, it will increase awareness and incentives for private industry and academia to collaborate with the government. The Model advocates for the Administration to adopt a targeted policy initiative (strategic development tool): the Matchmaker. The Matchmaker is a private-public equity investment fund that will invest in early-stage firms, while also addressing the commercial strategic development needs articulated by the public funding partners—a governmental agency. It will establish a channel for private firms to access government procurement and development. The initiative will function as an autonomous body, and be designed to prevent political capture. The adoption of the strategic Matchmaker fund will be to complement, and not to replace, the private market efforts in financing emerging growth firms

The Coalition Model, a Private-Public Strategic Innovation Policy Model for Encouraging Entrepreneurship and Economic Growth in the Era of New Economic Challenges

Innovation driven entrepreneurial firms have an important role in contributing to job creation, to generating technological innovation and to stimulating the United States economy. However, there is a notable recent decline in emerging growth entrepreneurial activity in the United States. The Coalition Model proposes ways to maximize opportunities for industry, academia and government to collaborate and build sustainable relationships, in order to help convert the current challenges in the U.S. market into opportunities. Designing a new innovation strategy policy will lead the United States in generating innovation, technology and economic growth, as well as help the federal government harness new approaches for institutional change. Adopting the Coalition Model bridges some of the financial inefficiencies and information gaps associated with investment in innovation driven enterprises, but, perhaps, more importantly, will serve as a catalyst for encouraging and stimulating development of new firms and technologies. The model is built on the notion of taking a proactive approach to innovation. Encouraging government agencies to fund research and innovation, by identifying specific technological challenges, determining the course of the research that can benefit their needs, collaborating with audiences in the public sector, research institutions and universities, and private corporations to act on these needs, and advance commercialization efforts. There are several potential benefits to adopting such a proactive policy

Reputation based Buyer Strategies for Seller Selection in Electronic Markets

Reputation based adaptive buying agents that reason about sellers for purchase decisions have been designed for B2C ecommerce markets. Previous research in the area of buyer agent strategies for choosing seller agents in ecommerce markets has focused on frequent purchases. In this thesis, we present reputation based strategies for buyer agents to choose seller agents in a decentralized multi agent based ecommerce markets for frequent as well as infrequent purchases. We consider a marketplace where the behavior of seller agents and buyer agents can vary, they can enter and leave the market any time, they may be dishonest, and quality of the product can be gauged after actually receiving the product. Buyer agents exchange seller agents' information, which is based on their own experiences, with other buyer agents in the market. However, there is no guarantee that when other buyer agents provide information, they are truthful or share similar opinions. First we present a method for buyer agent to model a seller agent's reputation. The buyer agent computes a seller agent's reputation based on its ability to meet its expectations of product quality and price as compared to its competitors. We show that a buying agent acting alone, utilizing our model of maintaining seller agents' reputation and buying strategy does better than buying agents acting alone employing strategies proposed previously by other researchers for frequent as well as for infrequent purchases. Next we present two methods for buyer agents to identify other trustworthy buyer agent friends who are honest and have similar opinions regarding seller agents, based on sharing of seller agents' information with each other. In the first method, buyer agent utilizes other buyer agents' opinions and ratings of seller agents to identify trustworthy buyer agent friends. Reputation of seller agents provided by trustworthy buyer agent friends is adjusted to account for the differences in the rating systems and combined with its own information on seller agents to choose high quality, low priced seller agent. In the second method, buyer agent only utilizes other buyer agents' opinions of seller agents to identify trustworthy buyer agent friends. Ratings are assigned to seller agents by the buyer agent based on trustworthy friend buyer agents' opinions and combined with its own rating on seller agents to choose a high quality, low priced seller agent to purchase from. We conducted experiments to show that both methods are successful in distinguishing between trustworthy buyer agent friends, whose opinions should be utilized in decision making, and untrustworthy buyer agent friends who are either dishonest, or have different opinions. We also show that buyer agents using our models of identifying trustworthy buyer agent friends have higher performance than a buyer agent acting alone for infrequent purchases and for increasing numbers of sellers in the market. Finally we analyze the performances of buyer agents with risk taking and conservative attitudes. A buyer agent with risk taking attitude considers a new seller agent as reputable initially and tends to purchase from a new seller agent if they are offering the lowest price among reputable seller agents. A buyer agent with conservative attitude is cautious in its approach and explores new seller agents at a rate proportional to the ratio of unexplored seller agents to the all the seller agents who have sent bids. Our results show that, when buyer agents are making decisions based on their own information, a buyer agent with conservative attitude has the best performance. When buyer agents are utilizing information provided by their trusted friends, a buyer agent with risk taking attitude and using only trusted friend buyer agents' opinions of seller agents has the best performance. In summary, the main contributions of this dissertation are: 1.A new reputation based way to model seller agents by buyer agents based on direct interactions. 2.A protocol to exchange reputation information about seller agents with other buyer agent friends based on the friends' direct interaction with seller agents. 3.Two methods of identifying trustworthy buyer agent friends who are honest and share similar opinions, and utilizing the information provided by them to maximize a buyer agent's chances of choosing a high quality, low priced seller agent to purchase from

Solve a Constraint Problem without Modeling It

International audienceWe study how to find a solution to a constraint problem without modeling it. Constraint acquisition systems such as Conacq or ModelSeeker are not able to solve a single instance of a problem because they require positive examples to learn. The recent QuAcq algorithm for constraint acquisition does not require positive examples to learn a constraint network. It is thus able to solve a constraint problem without modeling it: we simply exit from QuAcq as soon as a complete example is classified as positive by the user. In this paper, we propose ASK&SOLVE, an elicitation-based solver that tries to find the best tradeoff between learning and solving to converge as soon as possible on a solution. We propose several strategies to speed-up ASK&SOLVE. Finally we give an experimental evaluation that shows that our approach improves the state of the art

Semantic search and composition in unstructured peer-to-peer networks

This dissertation focuses on several research questions in the area of semantic search and composition in unstructured peer-to-peer (P2P) networks. Going beyond the state of the art, the proposed semantic-based search strategy S2P2P offers a novel path-suggestion based query routing mechanism, providing a reasonable tradeoff between search performance and network traffic overhead. In addition, the first semantic-based data replication scheme DSDR is proposed. It enables peers to use semantic information to select replica numbers and target peers to address predicted future demands. With DSDR, k-random search can achieve better precision and recall than it can with a near-optimal non-semantic replication strategy. Further, this thesis introduces a functional automatic semantic service composition method, SPSC. Distinctively, it enables peers to jointly compose complex workflows with high cumulative recall but low network traffic overhead, using heuristic-based bidirectional haining and service memorization mechanisms. Its query branching method helps to handle dead-ends in a pruned search space. SPSC is proved to be sound and a lower bound of is completeness is given. Finally, this thesis presents iRep3D for semantic-index based 3D scene selection in P2P search. Its efficient retrieval scales to answer hybrid queries involving conceptual, functional and geometric aspects. iRep3D outperforms previous representative efforts in terms of search precision and efficiency.Diese Dissertation bearbeitet Forschungsfragen zur semantischen Suche und Komposition in unstrukturierten Peer-to-Peer Netzen(P2P). Die semantische Suchstrategie S2P2P verwendet eine neuartige Methode zur Anfrageweiterleitung basierend auf Pfadvorschlägen, welche den Stand der Wissenschaft übertrifft. Sie bietet angemessene Balance zwischen Suchleistung und Kommunikationsbelastung im Netzwerk. Außerdem wird das erste semantische System zur Datenreplikation genannt DSDR vorgestellt, welche semantische Informationen berücksichtigt vorhergesagten zukünftigen Bedarf optimal im P2P zu decken. Hierdurch erzielt k-random-Suche bessere Präzision und Ausbeute als mit nahezu optimaler nicht-semantischer Replikation. SPSC, ein automatisches Verfahren zur funktional korrekten Komposition semantischer Dienste, ermöglicht es Peers, gemeinsam komplexe Ablaufpläne zu komponieren. Mechanismen zur heuristischen bidirektionalen Verkettung und Rückstellung von Diensten ermöglichen hohe Ausbeute bei geringer Belastung des Netzes. Eine Methode zur Anfrageverzweigung vermeidet das Feststecken in Sackgassen im beschnittenen Suchraum. Beweise zur Korrektheit und unteren Schranke der Vollständigkeit von SPSC sind gegeben. iRep3D ist ein neuer semantischer Selektionsmechanismus für 3D-Modelle in P2P. iRep3D beantwortet effizient hybride Anfragen unter Berücksichtigung konzeptioneller, funktionaler und geometrischer Aspekte. Der Ansatz übertrifft vorherige Arbeiten bezüglich Präzision und Effizienz

Combining Coordination and Organisation Mechanisms for the Development of a Dynamic Context-aware Information System Personalised by means of Logic-based Preference Methods

The general objective of this thesis is to enhance current ICDs by developing a personalised information system stable over dynamic and open environments, by adapting the behaviour to different situations, and handle user preferences in order to effectively provide the content (by means of a composition of several information services) the user is waiting for. Thus, the system combines two different usage contexts: the adaptive behaviour, in which the system adapts to unexpected events (e.g., the sudden failure of a service selected as information source), and the information customisation, in which the system proactively personalises a list of suggestions by considering user’s context and preferences

Semantic search and composition in unstructured peer-to-peer networks

This dissertation focuses on several research questions in the area of semantic search and composition in unstructured peer-to-peer (P2P) networks. Going beyond the state of the art, the proposed semantic-based search strategy S2P2P offers a novel path-suggestion based query routing mechanism, providing a reasonable tradeoff between search performance and network traffic overhead. In addition, the first semantic-based data replication scheme DSDR is proposed. It enables peers to use semantic information to select replica numbers and target peers to address predicted future demands. With DSDR, k-random search can achieve better precision and recall than it can with a near-optimal non-semantic replication strategy. Further, this thesis introduces a functional automatic semantic service composition method, SPSC. Distinctively, it enables peers to jointly compose complex workflows with high cumulative recall but low network traffic overhead, using heuristic-based bidirectional haining and service memorization mechanisms. Its query branching method helps to handle dead-ends in a pruned search space. SPSC is proved to be sound and a lower bound of is completeness is given. Finally, this thesis presents iRep3D for semantic-index based 3D scene selection in P2P search. Its efficient retrieval scales to answer hybrid queries involving conceptual, functional and geometric aspects. iRep3D outperforms previous representative efforts in terms of search precision and efficiency.Diese Dissertation bearbeitet Forschungsfragen zur semantischen Suche und Komposition in unstrukturierten Peer-to-Peer Netzen(P2P). Die semantische Suchstrategie S2P2P verwendet eine neuartige Methode zur Anfrageweiterleitung basierend auf Pfadvorschlägen, welche den Stand der Wissenschaft übertrifft. Sie bietet angemessene Balance zwischen Suchleistung und Kommunikationsbelastung im Netzwerk. Außerdem wird das erste semantische System zur Datenreplikation genannt DSDR vorgestellt, welche semantische Informationen berücksichtigt vorhergesagten zukünftigen Bedarf optimal im P2P zu decken. Hierdurch erzielt k-random-Suche bessere Präzision und Ausbeute als mit nahezu optimaler nicht-semantischer Replikation. SPSC, ein automatisches Verfahren zur funktional korrekten Komposition semantischer Dienste, ermöglicht es Peers, gemeinsam komplexe Ablaufpläne zu komponieren. Mechanismen zur heuristischen bidirektionalen Verkettung und Rückstellung von Diensten ermöglichen hohe Ausbeute bei geringer Belastung des Netzes. Eine Methode zur Anfrageverzweigung vermeidet das Feststecken in Sackgassen im beschnittenen Suchraum. Beweise zur Korrektheit und unteren Schranke der Vollständigkeit von SPSC sind gegeben. iRep3D ist ein neuer semantischer Selektionsmechanismus für 3D-Modelle in P2P. iRep3D beantwortet effizient hybride Anfragen unter Berücksichtigung konzeptioneller, funktionaler und geometrischer Aspekte. Der Ansatz übertrifft vorherige Arbeiten bezüglich Präzision und Effizienz

MANAGERS\u27 NETWORK CHANGE AND THEIR PROMOTABILITY DURING A MERGER

I investigate whether cross-functional or cross-organizational networking following a large corporate merger and acquisition improves managers’ career outcomes. Previous research on networks and career success has focused on stable organizational environments, finding that large, open networks with many structural holes are most advantageous because of superior information benefits and control power, while closed networks provide redundant information that is unhelpful career-wise. However, I suggest that while dense, closed networks formed within knowledge (functional) or identity (legacy organization) boundaries might be detrimental to executives’ future promotability, closed networks are helpful if they are created across those boundaries. These ties help to facilitate knowledge transfer and develop a new superordinate post-merger identity and are ultimately valued by the organization. I tested this on junior executives’ email and survey data collected at two time points (pre-merger and a year later) from a newly-merged organization. Results show that while closed networks with higher constraint in general were detrimental to executive’s promotability pre-merger, they lose the negative effect in the post-merger tumult. Controlling for overall network constraint, increasing closed networks across functional and legacy organizational boundaries led to managers receiving higher promotability evaluations from top management, whereas increasing closed networks within one functional and legacy organizational boundary did not have a significant impact. Managers’ rank and networking strategy that joins other employees (i.e., having a tertius iungens orientation) 2 moderated the relationships between networks and promotability. Implications are discussed for career and social networks research

The matchmaking industry and singles culture in Britain, 1970-2000

This thesis charts the expansion of the British dating industry after 1970, using singles services as a lens for assessing the impact of a period of rapid sexual and gender-political change on romantic aspiration. Its central contention is that by studying mediated courtship, we gain a new window onto the very heart of change in late 20th century Britain – namely, the transformation of the gender order. Courtship lets us see how this transformation – normally studied in political, sexual, demographic or cultural terms – was played out in the everyday affective and social lives of individuals. The thesis is arranged in four chapters, with sources centring on first person testimony (Mass Observation diaries, oral history, reader letters, television interviews, memoirs) and newspaper discourse. The first chapter discusses the demographic, cultural and discursive context in which Britain’s expanding population of single people increasingly sought commercial, third-party romantic aid. Chapters Two and Three set out the structure of the mediated dating industry and its most prominent characters alongside an analysis of the flashpoints that shaped how it was perceived. In the final chapter, I turn to daters’ memory and experience in terms of their self-identifications, expectations and encounters. The evidence analysed here is used to argue for the emergence of a new and distinctive ‘single’ identity in the period. Moreover, by interrogating the production of romance and the conditions in which it could take place, I show that at the heart of heterosexuality in late 20th century Britain there existed a relationship between rapid change and older feelings. This dynamic has not so far been adequately accounted for by historians and is, I argue, integral to a full understanding of relational life in the period