Visible to the public Biblio

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2020-02-10
Hoey, Jesse, Sheikhbahaee, Zahra, MacKinnon, Neil J..  2019.  Deliberative and Affective Reasoning: a Bayesian Dual-Process Model. 2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW). :388–394.
The presence of artificial agents in human social networks is growing. From chatbots to robots, human experience in the developed world is moving towards a socio-technical system in which agents can be technological or biological, with increasingly blurred distinctions between. Given that emotion is a key element of human interaction, enabling artificial agents with the ability to reason about affect is a key stepping stone towards a future in which technological agents and humans can work together. This paper presents work on building intelligent computational agents that integrate both emotion and cognition. These agents are grounded in the well-established social-psychological Bayesian Affect Control Theory (BayesAct). The core idea of BayesAct is that humans are motivated in their social interactions by affective alignment: they strive for their social experiences to be coherent at a deep, emotional level with their sense of identity and general world views as constructed through culturally shared symbols. This affective alignment creates cohesive bonds between group members, and is instrumental for collaborations to solidify as relational group commitments. BayesAct agents are motivated in their social interactions by a combination of affective alignment and decision theoretic reasoning, trading the two off as a function of the uncertainty or unpredictability of the situation. This paper provides a high-level view of dual process theories and advances BayesAct as a plausible, computationally tractable model based in social-psychological and sociological theory.
Barnes, Chloe M., Ekárt, Anikó, Lewis, Peter R..  2019.  Social Action in Socially Situated Agents. 2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO). :97–106.
Two systems pursuing their own goals in a shared world can interact in ways that are not so explicit - such that the presence of another system alone can interfere with how one is able to achieve its own goals. Drawing inspiration from human psychology and the theory of social action, we propose the notion of employing social action in socially situated agents as a means of alleviating interference in interacting systems. Here we demonstrate that these specific issues of behavioural and evolutionary instability caused by the unintended consequences of interactions can be addressed with agents capable of a fusion of goal-rationality and traditional action, resulting in a stable society capable of achieving goals during the course of evolution.
Carneiro, Lucas R., Delgado, Carla A.D.M., da Silva, João C.P..  2019.  Social Analysis of Game Agents: How Trust and Reputation can Improve Player Experience. 2019 8th Brazilian Conference on Intelligent Systems (BRACIS). :485–490.
Video games normally use Artificial Intelligence techniques to improve Non-Player Character (NPC) behavior, creating a more realistic experience for their players. However, rational behavior in general does not consider social interactions between player and bots. Because of that, a new framework for NPCs was proposed, which uses a social bias to mix the default strategy of finding the best possible plays to win with a analysis to decide if other players should be categorized as allies or foes. Trust and reputation models were used together to implement this demeanor. In this paper we discuss an implementation of this framework inside the game Settlers of Catan. New NPC agents are created to this implementation. We also analyze the results obtained from simulations among agents and players to conclude how the use of trust and reputation in NPCs can create a better gaming experience.
Chen, Siyuan, Liu, Wei, Liu, Jiamou, Soo, Khí-Uí, Chen, Wu.  2019.  Maximizing Social Welfare in Fractional Hedonic Games using Shapley Value. 2019 IEEE International Conference on Agents (ICA). :21–26.
Fractional hedonic games (FHGs) are extensively studied in game theory and explain the formation of coalitions among individuals in a group. This paper investigates the coalition generation problem, namely, finding a coalition structure whose social welfare, i.e., the sum of the players' payoffs, is maximized. We focus on agent-based methods which set the decision rules for each player in the game. Through repeated interactions the players arrive at a coalition structure. In particular, we propose CFSV, namely, coalition formation with Shapley value-based welfare distribution scheme. To evaluate CFSV, we theoretically demonstrate that this algorithm achieves optimal coalition structure over certain standard graph classes and empirically compare the algorithm against other existing benchmarks on real-world and synthetic graphs. The results show that CFSV is able to achieve superior performance.
Salehi, Sajjad, Taghiyareh, Fattaneh.  2019.  Introspective Agents in Opinion Formation Modeling to Predict Social Market. 2019 5th International Conference on Web Research (ICWR). :28–34.
Individuals may change their opinion in effect of a wide range of factors like interaction with peer groups, governmental policies and personal intentions. Works in this area mainly focus on individuals in social network and their interactions while neglect other factors. In this paper we have introduced an opinion formation model that consider the internal tendency as a personal feature of individuals in social network. In this model agents may trust, distrust or be neutral to their neighbors. They modify their opinion based on the opinion of their neighbors, trust/distrust to them while considering the internal tendency. The results of simulation show that this model can predict the opinion of social network especially when the average of nodal degree and clustering coefficient are high enough. Since this model can predict the preferences of individuals in market, it can be used to define marketing and production strategy.
2020-01-20
Ou, Chung-Ming.  2019.  Host-based Intrusion Detection Systems Inspired by Machine Learning of Agent-Based Artificial Immune Systems. 2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA). :1–5.

An adaptable agent-based IDS (AAIDS) inspired by the danger theory of artificial immune system is proposed. The learning mechanism of AAIDS is designed by emulating how dendritic cells (DC) in immune systems detect and classify danger signals. AG agent, DC agent and TC agent coordinate together and respond to system calls directly rather than analyze network packets. Simulations show AAIDS can determine several critical scenarios of the system behaviors where packet analysis is impractical.

Waqar, Ali, Hu, Junjie, Mushtaq, Muhammad Rizwan, Hussain, Hadi, Qazi, Hassaan Aziz.  2019.  Energy Management in an Islanded Microgrid: A Consensus Theory Approach. 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET). :1–6.

This article presents a consensus based distributed energy management optimization algorithm for an islanded microgrid. With the rapid development of renewable energy and distributed generation (DG) energy management is becoming more and more distributed. To solve this problem a multi-agent system based distributed solution is designed in this work which uses lambda-iteration method to solve optimization problem. Moreover, the algorithm is fully distributed and transmission losses are also considered in the modeling process which enhanced the practicality of proposed work. Simulations are performed for different cases on 8-bus microgrid to show the effectiveness of algorithm. Moreover, a scalability test is performed at the end to further justify the expandability performance of algorithm for more advanced networks.

2019-12-16
Xue, Zijun, Ko, Ting-Yu, Yuchen, Neo, Wu, Ming-Kuang Daniel, Hsieh, Chu-Cheng.  2018.  Isa: Intuit Smart Agent, A Neural-Based Agent-Assist Chatbot. 2018 IEEE International Conference on Data Mining Workshops (ICDMW). :1423–1428.
Hiring seasonal workers in call centers to provide customer service is a common practice in B2C companies. The quality of service delivered by both contracting and employee customer service agents depends heavily on the domain knowledge available to them. When observing the internal group messaging channels used by agents, we found that similar questions are often asked repetitively by different agents, especially from less experienced ones. The goal of our work is to leverage the promising advances in conversational AI to provide a chatbot-like mechanism for assisting agents in promptly resolving a customer's issue. In this paper, we develop a neural-based conversational solution that employs BiLSTM with attention mechanism and demonstrate how our system boosts the effectiveness of customer support agents. In addition, we discuss the design principles and the necessary considerations for our system. We then demonstrate how our system, named "Isa" (Intuit Smart Agent), can help customer service agents provide a high-quality customer experience by reducing customer wait time and by applying the knowledge accumulated from customer interactions in future applications.
Karve, Shreya, Nagmal, Arati, Papalkar, Sahil, Deshpande, S. A..  2018.  Context Sensitive Conversational Agent Using DNN. 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA). :475–478.
We investigate a method of building a closed domain intelligent conversational agent using deep neural networks. A conversational agent is a dialog system intended to converse with a human, with a coherent structure. Our conversational agent uses a retrieval based model that identifies the intent of the input user query and maps it to a knowledge base to return appropriate results. Human conversations are based on context, but existing conversational agents are context insensitive. To overcome this limitation, our system uses a simple stack based context identification and storage system. The conversational agent generates responses according to the current context of conversation. allowing more human-like conversations.
Alam, Mehreen.  2018.  Neural Encoder-Decoder based Urdu Conversational Agent. 2018 9th IEEE Annual Ubiquitous Computing, Electronics Mobile Communication Conference (UEMCON). :901–905.
Conversational agents have very much become part of our lives since the renaissance of neural network based "neural conversational agents". Previously used manually annotated and rule based methods lacked the scalability and generalization capabilities of the neural conversational agents. A neural conversational agent has two parts: at one end an encoder understands the question while the other end a decoder prepares and outputs the corresponding answer to the question asked. Both the parts are typically designed using recurrent neural network and its variants and trained in an end-to-end fashion. Although conversation agents for other languages have been developed, Urdu language has seen very less progress in building of conversational agents. Especially recent state of the art neural network based techniques have not been explored yet. In this paper, we design an attention driven deep encoder-decoder based neural conversational agent for Urdu language. Overall, we make following contributions we (i) create a dataset of 5000 question-answer pairs, and (ii) present a new deep encoder-decoder based conversational agent for Urdu language. For our work, we limit the knowledge base of our agent to general knowledge regarding Pakistan. Our best model has the BLEU score of 58 and gives syntactically and semantically correct answers in majority of the cases.
2019-12-09
Tsochev, Georgi, Trifonov, Roumen, Yoshinov, Radoslav, Manolov, Slavcho, Pavlova, Galya.  2019.  Improving the Efficiency of IDPS by Using Hybrid Methods from Artificial Intelligence. 2019 International Conference on Information Technologies (InfoTech). :1-4.

The present paper describes some of the results obtained in the Faculty of Computer Systems and Technology at Technical University of Sofia in the implementation of project related to the application of intelligent methods for increasing the security in computer networks. Also is made a survey about existing hybrid methods, which are using several artificial intelligent methods for cyber defense. The paper introduces a model for intrusion detection systems where multi agent systems are the bases and artificial intelligence are applicable by the means simple real-time models constructed in laboratory environment.

Li, Wenjuan, Cao, Jian, Hu, Keyong, Xu, Jie, Buyya, Rajkumar.  2019.  A Trust-Based Agent Learning Model for Service Composition in Mobile Cloud Computing Environments. IEEE Access. 7:34207–34226.
Mobile cloud computing has the features of resource constraints, openness, and uncertainty which leads to the high uncertainty on its quality of service (QoS) provision and serious security risks. Therefore, when faced with complex service requirements, an efficient and reliable service composition approach is extremely important. In addition, preference learning is also a key factor to improve user experiences. In order to address them, this paper introduces a three-layered trust-enabled service composition model for the mobile cloud computing systems. Based on the fuzzy comprehensive evaluation method, we design a novel and integrated trust management model. Service brokers are equipped with a learning module enabling them to better analyze customers' service preferences, especially in cases when the details of a service request are not totally disclosed. Because traditional methods cannot totally reflect the autonomous collaboration between the mobile cloud entities, a prototype system based on the multi-agent platform JADE is implemented to evaluate the efficiency of the proposed strategies. The experimental results show that our approach improves the transaction success rate and user satisfaction.
2019-11-26
Tenorio-Fornés, Antonio, Hassan, Samer, Pavón, Juan.  2018.  Open Peer-to-Peer Systems over Blockchain and IPFS: An Agent Oriented Framework. Proceedings of the 1st Workshop on Cryptocurrencies and Blockchains for Distributed Systems. :19-24.

In recent years, the increasing concerns around the centralized cloud web services (e.g. privacy, governance, surveillance, security) have triggered the emergence of new distributed technologies, such as IPFS or the Blockchain. These innovations have tackled technical challenges that were unresolved until their appearance. Existing models of peer-to-peer systems need a revision to cover the spectrum of potential systems that can be now implemented as peer-to-peer systems. This work presents a framework to build these systems. It uses an agent-oriented approach in an open environment where agents have only partial information of the system data. The proposal covers data access, data discovery and data trust in peer-to-peer systems where different actors may interact. Moreover, the framework proposes a distributed architecture for these open systems, and provides guidelines to decide in which cases Blockchain technology may be required, or when other technologies may be sufficient.

2019-09-05
Panfili, M., Giuseppi, A., Fiaschetti, A., Al-Jibreen, H. B., Pietrabissa, A., Priscoli, F. Delli.  2018.  A Game-Theoretical Approach to Cyber-Security of Critical Infrastructures Based on Multi-Agent Reinforcement Learning. 2018 26th Mediterranean Conference on Control and Automation (MED). :460-465.

This paper presents a control strategy for Cyber-Physical System defense developed in the framework of the European Project ATENA, that concerns Critical Infrastructure (CI) protection. The aim of the controller is to find the optimal security configuration, in terms of countermeasures to implement, in order to address the system vulnerabilities. The attack/defense problem is modeled as a multi-agent general sum game, where the aim of the defender is to prevent the most damage possible by finding an optimal trade-off between prevention actions and their costs. The problem is solved utilizing Reinforcement Learning and simulation results provide a proof of the proposed concept, showing how the defender of the protected CI is able to minimize the damage caused by his her opponents by finding the Nash equilibrium of the game in the zero-sum variant, and, in a more general scenario, by driving the attacker in the position where the damage she/he can cause to the infrastructure is lower than the cost it has to sustain to enforce her/his attack strategy.

2019-02-25
Hassan, M. H., Mostafa, S. A., Mustapha, A., Wahab, M. H. Abd, Nor, D. Md.  2018.  A Survey of Multi-Agent System Approach in Risk Assessment. 2018 International Symposium on Agent, Multi-Agent Systems and Robotics (ISAMSR). :1–6.
Risk Assessment is a foundation of decision-making about a future project behaviour or action. The related decision made might entail further analyzes to perform risk- reduction. The risk is a general phenomenon that takes different depicts and types. Static risk and its circumstances do not significantly change over time while dynamic risk arises out of the changes in interrelated circumstances. A Multi-Agent System (MAS) approach has become a popular tool to tackle different problems that relate to risk. The MAS helps in the decision aid processes and when responding to the consequences of the risk. This paper surveys some of the existing methods and techniques of risk assessment in different application domains. The survey focuses on the employment of MAS approach in risk assessment. The survey outcomes an illustration of the roles and contributions of the MAS in the Dynamic Risk Assessment (DRA) field.
2019-02-21
Shimada, Koki, Bentley, Peter.  2018.  Learning How to Flock: Deriving Individual Behaviour from Collective Behaviour with Multi-agent Reinforcement Learning and Natural Evolution Strategies. Proceedings of the Genetic and Evolutionary Computation Conference Companion. :169–170.
This work proposes a method for predicting the internal mechanisms of individual agents using observed collective behaviours by multi-agent reinforcement learning (MARL). Since the emergence of group behaviour among many agents can undergo phase transitions, and the action space will not in general be smooth, natural evolution strategies were adopted for updating a policy function. We tested the approach using a well-known flocking algorithm as a target model for our system to learn. With the data obtained from this rule-based model, the MARL model was trained, and its acquired behaviour was compared to the original. In the process, we discovered that agents trained by MARL can self-organize flow patterns using only local information. The expressed pattern is robust to changes in the initial positions of agents, whilst being sensitive to the training conditions used.
2018-12-03
Larsson, A., Ibrahim, O., Olsson, L., Laere, J. van.  2017.  Agent based simulation of a payment system for resilience assessments. 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). :314–318.

We provide an agent based simulation model of the Swedish payment system. The simulation model is to be used to analyze the consequences of loss of functionality, or disruptions of the payment system for the food and fuel supply chains as well as the bank sector. We propose a gaming simulation approach, using a computer based role playing game, to explore the collaborative responses from the key actors, in order to evoke and facilitate collective resilience.

2018-09-12
Januário, Fábio, Cardoso, Alberto, Gil, Paulo.  2017.  A Multi-Agent Framework for Resilient Enhancement in Networked Control Systems. Proceedings of the 9th International Conference on Computer and Automation Engineering. :291–295.
Recent advances on the integration of control systems with state of the art information technologies have brought into play new uncertainties, not only associated with the physical world, but also from a cyber-space's perspective. In cyber-physical environments, awareness and resilience are invaluable properties. The paper focuses on the development of an architecture relying on a hierarchical multi-agent framework for resilience enhancement. This framework was evaluated on a test-bed comprising several distributed computational devices and heterogeneous communications. Results from tests prove the relevance of the proposed approach.
2018-08-23
Felmlee, D., Lupu, E., McMillan, C., Karafili, E., Bertino, E..  2017.  Decision-making in policy governed human-autonomous systems teams. 2017 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computed, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). :1–6.

Policies govern choices in the behavior of systems. They are applied to human behavior as well as to the behavior of autonomous systems but are defined differently in each case. Generally humans have the ability to interpret the intent behind the policies, to bring about their desired effects, even occasionally violating them when the need arises. In contrast, policies for automated systems fully define the prescribed behavior without ambiguity, conflicts or omissions. The increasing use of AI techniques and machine learning in autonomous systems such as drones promises to blur these boundaries and allows us to conceive in a similar way more flexible policies for the spectrum of human-autonomous systems collaborations. In coalition environments this spectrum extends across the boundaries of authority in pursuit of a common coalition goal and covers collaborations between human and autonomous systems alike. In social sciences, social exchange theory has been applied successfully to explain human behavior in a variety of contexts. It provides a framework linking the expected rewards, costs, satisfaction and commitment to explain and anticipate the choices that individuals make when confronted with various options. We discuss here how it can be used within coalition environments to explain joint decision making and to help formulate policies re-framing the concepts where appropriate. Social exchange theory is particularly attractive within this context as it provides a theory with “measurable” components that can be readily integrated in machine reasoning processes.

2018-05-30
An, S., Zhao, Z., Zhou, H..  2017.  Research on an Agent-Based Intelligent Social Tagging Recommendation System. 2017 9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). 1:43–46.

With the repaid growth of social tagging users, it becomes very important for social tagging systems how the required resources are recommended to users rapidly and accurately. Firstly, the architecture of an agent-based intelligent social tagging system is constructed using agent technology. Secondly, the design and implementation of user interest mining, personalized recommendation and common preference group recommendation are presented. Finally, a self-adaptive recommendation strategy for social tagging and its implementation are proposed based on the analysis to the shortcoming of the personalized recommendation strategy and the common preference group recommendation strategy. The self-adaptive recommendation strategy achieves equilibrium selection between efficiency and accuracy, so that it solves the contradiction between efficiency and accuracy in the personalized recommendation model and the common preference recommendation model.

Misra, G., Such, J. M..  2017.  PACMAN: Personal Agent for Access Control in Social Media. IEEE Internet Computing. 21:18–26.

Given social media users' plethora of interactions, appropriately controlling access to such information becomes a challenging task for users. Selecting the appropriate audience, even from within their own friend network, can be fraught with difficulties. PACMAN is a potential solution for this dilemma problem. It's a personal assistant agent that recommends personalized access control decisions based on the social context of any information disclosure by incorporating communities generated from the user's network structure and utilizing information in the user's profile. PACMAN provides accurate recommendations while minimizing intrusiveness.

2018-03-19
Bulusu, S. T., Laborde, R., Wazan, A. S., Barrere, F., Benzekri, A..  2017.  Describing Advanced Persistent Threats Using a Multi-Agent System Approach. 2017 1st Cyber Security in Networking Conference (CSNet). :1–3.

Advanced Persistent Threats are increasingly becoming one of the major concerns to many industries and organizations. Currently, there exists numerous articles and industrial reports describing various case studies of recent notable Advanced Persistent Threat attacks. However, these documents are expressed in natural language. This limits the efficient reusability of the threat intelligence information due to ambiguous nature of the natural language. In this article, we propose a model to formally represent Advanced Persistent Threats as multi-agent systems. Our model is inspired by the concepts of agent-oriented social modelling approaches, generally used for software security requirement analysis.

2018-01-10
Bönsch, Andrea, Trisnadi, Robert, Wendt, Jonathan, Vierjahn, Tom, Kuhlen, Torsten W..  2017.  Score-based Recommendation for Efficiently Selecting Individual Virtual Agents in Multi-agent Systems. Proceedings of the 23rd ACM Symposium on Virtual Reality Software and Technology. :74:1–74:2.
Controlling user-agent-interactions by means of an external operator includes selecting the virtual interaction partners fast and faultlessly. However, especially in immersive scenes with a large number of potential partners, this task is non-trivial. Thus, we present a score-based recommendation system supporting an operator in the selection task. Agents are recommended as potential partners based on two parameters: the user's distance to the agents and the user's gazing direction. An additional graphical user interface (GUI) provides elements for configuring the system and for applying actions to those agents which the operator has confirmed as interaction partners.
2017-12-12
Davis, D. B., Featherston, J., Fukuda, M., Asuncion, H. U..  2017.  Data Provenance for Multi-Agent Models. 2017 IEEE 13th International Conference on e-Science (e-Science). :39–48.

Multi-agent simulations are useful for exploring collective patterns of individual behavior in social, biological, economic, network, and physical systems. However, there is no provenance support for multi-agent models (MAMs) in a distributed setting. To this end, we introduce ProvMASS, a novel approach to capture provenance of MAMs in a distributed memory by combining inter-process identification, lightweight coordination of in-memory provenance storage, and adaptive provenance capture. ProvMASS is built on top of the Multi-Agent Spatial Simulation (MASS) library, a framework that combines multi-agent systems with large-scale fine-grained agent-based models, or MAMs. Unlike other environments supporting MAMs, MASS parallelizes simulations with distributed memory, where agents and spatial data are shared application resources. We evaluate our approach with provenance queries to support three use cases and performance measures. Initial results indicate that our approach can support various provenance queries for MAMs at reasonable performance overhead.

2017-03-08
Tatarenko, T..  2015.  1-recall reinforcement learning leading to an optimal equilibrium in potential games with discrete and continuous actions. 2015 54th IEEE Conference on Decision and Control (CDC). :6749–6754.

Game theory serves as a powerful tool for distributed optimization in multiagent systems in different applications. In this paper we consider multiagent systems that can be modeled as a potential game whose potential function coincides with a global objective function to be maximized. This approach renders the agents the strategic decision makers and the corresponding optimization problem the problem of learning an optimal equilibruim point in the designed game. In distinction from the existing works on the topic of payoff-based learning, we deal here with the systems where agents have neither memory nor ability for communication, and they base their decision only on the currently played action and the experienced payoff. Because of these restrictions, we use the methods of reinforcement learning, stochastic approximation, and learning automata extensively reviewed and analyzed in [3], [9]. These methods allow us to set up the agent dynamics that moves the game out of inefficient Nash equilibria and leads it close to an optimal one in both cases of discrete and continuous action sets.