A Neural Attention Model for Adaptive Learning of Social Friends' Preferences

Social-based recommendation systems exploit the selections of friends to combat the data sparsity on user preferences, and improve the recommendation accuracy of the collaborative filtering strategy. The main challenge is to capture and weigh friends' preferences, as in practice they do necessarily match. In this paper, we propose a Neural Attention mechanism for Social collaborative filtering, namely NAS. We design a neural architecture, to carefully compute the non-linearity in friends' preferences by taking into account the social latent effects of friends on user behavior. In addition, we introduce a social behavioral attention mechanism to adaptively weigh the influence of friends on user preferences and consequently generate accurate recommendations. Our experiments on publicly available datasets demonstrate the effectiveness of the proposed NAS model over other state-of-the-art methods. Furthermore, we study the effect of the proposed social behavioral attention mechanism and show that it is a key factor to our model's performance

Exploiting Domain Knowledge in Making Delegation Decisions

@inproceedings{conf/admi/EmeleNSP11, added-at = {2011-12-19T00:00:00.000+0100}, author = {Emele, Chukwuemeka David and Norman, Timothy J. and Sensoy, Murat and Parsons, Simon}, biburl = {http://www.bibsonomy.org/bibtex/20a08b683088443f1fd36d6ef28bf6615/dblp}, booktitle = {ADMI}, crossref = {conf/admi/2011}, editor = {Cao, Longbing and Bazzan, Ana L. C. and Symeonidis, Andreas L. and Gorodetsky, Vladimir and Weiss, Gerhard and Yu, Philip S.}, ee = {http://dx.doi.org/10.1007/978-3-642-27609-5_9}, interhash = {1d7e7f8554e8bdb3d43c32e02aeabcec}, intrahash = {0a08b683088443f1fd36d6ef28bf6615}, isbn = {978-3-642-27608-8}, keywords = {dblp}, pages = {117-131}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, timestamp = {2011-12-19T00:00:00.000+0100}, title = {Exploiting Domain Knowledge in Making Delegation Decisions.}, url = {http://dblp.uni-trier.de/db/conf/admi/admi2011.html#EmeleNSP11}, volume = 7103, year = 2011 }Postprin

Can we detect harmony in artistic compositions? A machine learning approach

Harmony in visual compositions is a concept that cannot be defined or easily expressed mathematically, even by humans. The goal of the research described in this paper was to find a numerical representation of artistic compositions with different levels of harmony. We ask humans to rate a collection of grayscale images based on the harmony they convey. To represent the images, a set of special features were designed and extracted. By doing so, it became possible to assign objective measures to subjectively judged compositions. Given the ratings and the extracted features, we utilized machine learning algorithms to evaluate the efficiency of such representations in a harmony classification problem. The best performing model (SVM) achieved 80% accuracy in distinguishing between harmonic and disharmonic images, which reinforces the assumption that concept of harmony can be expressed in a mathematical way that can be assessed by humans.Comment: 9 pages, ICAART 202

Institutional and policy frameworks shaping the Wooden Multi-Storey Construction markets : A comparative case study on Austria and Finland

In the urbanizing society faced with the climate change challenge, wood has major potential as a low-carbon and renewable construction material. Yet, Wooden Multi-storey Construction (WMC) remains a niche even in countries with rich forest resources. This paper compares the institutional and policy setting and assesses the WMC growth prospects in Austria and Finland, based on expert interviews, Delphi surveys, and the review of secondary materials. Clear differences were detected in the policy frameworks and institutional settings between the two countries. The Austrian fairly informal and largely private sector driven approaches to promote the growth of the WMC sector seem to have had a rather similar effect on the markets, as the formal policy measures, typically driven by the public sector in Finland. In both countries, the interviewed experts suggested additional, but partly different, policy measures and institutional changes to accelerate WMC market diffusion. In spite of the increase in WMC activity within the past ten years, the WMC market share is likely to remain rather low by 2030 in both countries, as the institutional frameworks are not expected to change abruptly. However, the future market prospects appear to be somewhat more positive in Finland compared with Austria.Peer reviewe

Innovation governance in the forest sector : Reviewing concepts, trends and gaps

Innovation in the forest sector is a growing research interest and within this field, there is a growing attention for institutional, policy and societal dimensions and particular when it comes to the question of how to support innovativeness in the sector. This Special Issue therefore focuses on governance aspects, relating to and bridging business and political-institutional-societal levels. This includes social/societal factors, goals and implications that have recently been studied under the label of social innovation. Furthermore, the emergence of bioeconomy as a paradigm and policy goal has become a driver for a variety of innovation processes on company and institutional levels. Our article provides a tentative definition of & ldquo;innovation governance & rdquo; and attempts a stateof-art review of innovation governance research in the forest sector. For structuring the research field, we propose to distinguish between organizational/managerial, policy or innovation studies. For the forestry sector, specifically, we suggest to distinguish between studies focusing on (i) innovative governance of forest management and forest goods and services; on (ii) the governance of innovation processes as such, or (iii) on specific (transformational) approaches that may be derived from combined goals such as innovation governance for sustainability, regional development, or a bioeconomy. Studies in the forest sector are picking up new trends from innovation research that increasingly include the role of societal changes and various stakeholders such as civil society organizations and users. They also include public-private partnership models or participatory governance. We finally should not only look in how far research approaches from outside are applied in the sector but we believe that the sector could contribute much more to our general scientific knowledge on ways for a societal transformation to sustainability.Peer reviewe

Transfer Reinforcement Learning Based Negotiating Agent Framework

While achieving tremendous success, there is still a major issue standing out in the domain of automated negotiation: it is inefficient for a negotiating agent to learn a strategy from scratch when being faced with an unknown opponent. Transfer learning can alleviate this problem by utilizing the knowledge of previously learned policies to accelerate the current task learning. This work presents a novel Transfer Learning based Negotiating Agent (TLNAgent) framework that allows a negotiating agent to transfer previous knowledge from source strategies optimized by deep reinforcement learning, to boost its performance in new tasks. TLNAgent comprises three key components: the negotiation module, the adaptation module and the transfer module. To be specific, the negotiation module is responsible for interacting with the other agent during negotiation. The adaptation module measures the helpfulness of each source policy based on a fusion of two selection mechanisms. The transfer module is based on lateral connections between source and target networks and accelerates the agent’s training by transferring knowledge from the selected source strategy. Our comprehensive experiments clearly demonstrate that TL is effective in the context of automated negotiation, and TLNAgent outperforms state-of-the-art Automated Negotiating Agents Competition (ANAC) negotiating agents in various domains

Social innovation and its impacts in disadvantaged rural areas: a new evaluation framework

An agreed and well-consolidated evaluation framework for the assessment of social innovation (SI) and its impacts has not been developed yet, despite tentative made by scholars (e.g., Nicholls et al. 2015). The EU funded H2020 project SIMRA \u2013 Social Innovation in Marginalised Rural Areas (www.simra-h2020.eu) \u2013 aims to conceptualize an evaluation framework for SI initiatives in disadvantaged rural areas of Europe and non-EU Mediterranean countries. Within SIMRA, SI is defined as \u201cthe reconfiguring of social practices, in response to societal challenges, which seeks to enhance outcomes on societal well-being and necessarily includes the engagement of civil society actors\u201d (Polman et al., 2017). The evaluation framework has been co-constructed with project partners and a panel of international stakeholders in the fields of agriculture, forestry and rural development (Nijnik et al. 2019). It is structured into dimensions and sub-dimensions. It follows the phases of a SI initiative, from the trigger that generates the idea, to the reconfiguring process, and to its impacts. Eight tools for data collection have been developed, tested in pilot cases, and applied in 11 case studies. Empirical results allowed to set 166 indicators: 73 indicators describe the SI dimensions; 63 indicators analyse the process, the project and the whole SI initiative by following relevance, efficiency, effectiveness, impact and sustainability evaluation criteria (OECD, 1991 and 2010); 30 indicators focus on the key aspects of the SI SIMRA definition. Social Network Analysis helps in visualizing the increasing collaborative network of actors involved in the SI process, from core group composed by innovators and followers, to the reconfigured network with new project partners. The approach integrates qualitative-pure methods (e.g., focus group) with quantitative ones. The proposed evaluation framework would like to contribute to current debates, both within the scientific and practitioners\u2019 communities, on evidence-based policy and self-evaluation by rural development agencies

Hierarchical reinforcement learning for communicating agents

This paper proposes hierarchical reinforcement learning (RL) methods for communication in multiagent coordination problems modelled as Markov Decision Processes (MDPs). To bridge the gap between the MDP view and the methods used to specify communication protocols in multiagent systems (using logical conditions and propositional message structure), we utilise interaction frames as powerful policy abstractions that can be combined with case-based reasoning techniques. Also, we exploit the fact that breaking communication processes down to manageable “chunks ” of interaction sequences (as suggested by the interaction frames approach) naturally corresponds to methods proposed in the area of hierarchical RL. The approach is illustrated and validated through experiments in a complex application domain which prove that it is capable of handling large state and action spaces.

Contract-net-based learning in a user-adaptive interface agency

Lenzmann B, Wachsmuth I. Contract-net-based learning in a user-adaptive interface agency. In: Weiss G, ed. Distributed Artificial Intelligence Meets Machine Learning - Learning in Multi-Agent Environments. LNAI. Vol 1221. Berlin: Springer-Verlag; 1997: 202-222.This paper describes a multi-agent learning approach to adaptation to users' preferences realized by an interface agency. Using a contract-net-based negotiation technique, agents as contractors as well as managers negotiate with each other to pursue the overall goal of dynamic user adaptation. By learning from indirect user feedback, the adjustment of internal credit vectors and the assignment of contractors that gained maximal credit with respect to the user's current preferences, the preceding session, and current situational circumstances can be realized. In this way,user adaptation is achieved without accumulating explicit user models but by the use of implicit, distributed user models