cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5683/sp3/o7olzv
title: Data required for revisualization and modification of the images analyzed in:Structural Modeling of Bitter Taste Receptors Employing a Multi-Template Homology Approach for Ligand Development: The TAS2R14 Case Study Validated Against CryoEM Structures

description: This dataset includes the 3D geometries discussed in the article so that others can build on them or replicate the results as needed
DOI: 10.5683/sp3/phvb4k
title: Replication Data for: First identification of trigonelline in Guaiacum Officinale fruit and its dengue protease inhibition activity

description: NMR data for trigonelline
DOI: 10.5683/sp3/xm0wh8
title: Agent Scheduling in Opinion Dynamics: A Taxonomy and Comparison Using Generalized Models

description: Opinion Dynamics, Agent-Based Modeling, Scheduling, Asynchronous, Synchronous
DOI: 10.5683/sp3/8z9sm4
title: Multi-agent scheduling problems under multitasking

description: We consider a multitasking scheduling model with multiple agents, each of which has a set of tasks to perform on a cloudmanufacturing platform on a competitive basis. Each agent wishes to minimise its desirable objective function related tothe completion times of its own tasks only. However, the cloud manufacturing platform wishes to minimise the objectiveof one agent (being long-term critical agent), while keeping the objective of each of the other agents (being short-termone-off agents) within a given limit. The objective functions considered are the maximum of a regular function (associatedwith each task), the total completion time, and the weighted number of late jobs. Cloud manufacturing enables multitaskingscheduling, under which the processing of a selected task may be interrupted by other tasks that are available but unﬁnished.We ascertain the computational complexity status of each of the problems we consider and devise solution procedures,if viable, for them. We also conduct numerical studies to generate insights into the effects of multitasking on schedulingoutcomes, with which the decision maker can justify making investments to adopt or avoid multitasking.
DOI: 10.5683/sp3/fornao
title: A multi-agent based cooperative approach to scheduling and routing

description: •Developed a generic open source agent based system for scheduling and routing.
•Each agent can instantiate a different metaheuristic and local heuristic combination.
•Proposed system allows agents to share partial solutions during the search.
•A cooperative approach based on this generic system is evaluated on two domains.
•Improving results for vehicle routing and up to date for permutation flowshop.
DOI: 10.5683/sp3/vqmbq4
title: Multi-agent-based proactive–reactive scheduling for a job shop

description: Multi-agent-based proactive–reactive scheduling for job shops is presented, aiming to hedge against the uncertainties of dynamic manufacturing environments. This scheduling mechanism consists of two stages, including proactive scheduling stage and reactive scheduling stage. In the proactive scheduling stage, the objective is to generate a robust predictive schedule against known uncertainties; in the reactive scheduling stage, the objective is to dynamically rectify the predictive schedule to adapt to unknown uncertainties, viz. the reactive scheduling stage is actually complementary to the proactive scheduling stage. A stochastic model is presented, which concerns uncertain processing times in proactive scheduling stage on the basis of analyzing the deficiencies of a classical scheduling model for a production schedule in practice. For the stochastic scheduling problem, a multi-agent-based architecture is proposed and a distributed scheduling algorithm is used to solve this stochastic problem. Finally, the repair strategies are introduced to maintain the original proactive schedule when unexpected events occur. Case study examples show that this scheduling mechanism generates more robust schedules than the classical scheduling mechanism.
DOI: 10.5683/sp3/cktxtp
title: On the Use of Asset Administration Shell for Modeling and Deploying Production Scheduling Agents within a Multi-Agent System

description: Industry 4.0 (I4.0) aims at achieving the interconnectivity of multiple industrial assets from different hierarchical layers within a manufacturing environment. The Asset Administration Shell (AAS) is a pilar component of I4.0 for the digital representation of assets and can be applied in both physical and digital assets, such as enterprise software, artificial intelligence (AI) agents, and databases. Multi-agent systems (MASs), in particular, are useful in the decentralized optimization of complex problems and applicable in various planning or scheduling scenarios that require the system’s ability to adapt to any given problem by using different optimization methods. In order to achieve this, a universal model for the agent’s information, communication, and behaviors should be provided in a way that is interoperable with the rest of the I4.0 assets and agents. To address these challenges, this work proposes an AAS-based information model for the description of scheduling agents. It allows multiple AI methods for scheduling, such as heuristics, mathematical programming, and deep reinforcement learning, to be encapsulated within a single agent, making it adjustable to different production scenarios. The software implementation of the proposed architecture aims to provide granularity in the deployment of scheduling agents which utilize the underlying AAS metamodel. The agent was implemented using the SARL agent-oriented programming (AOP) language and deployed in an open-source MAS platform. The system evaluation in a real-life bicycle production scenario indicated the agent’s ability to adapt and provide fast and accurate scheduling results.
DOI: 10.5683/sp3/ato1sv
title: Multi-agent systems negotiation to deal with dynamic scheduling in disturbed industrial context

description: It is now accepted that using multi-agent systems improve the reactivity to treat perturbation(s) within flexible manufacturing system. Intelligent algorithms shall be used to address these perturbation(s) and all smart decision entities within their environment have to continuously negotiate until their common and final goal is achieved. This paper proposes a negotiation-based control approach to deal with variability on a manufacturing system. It has initially formulated and modeled an environment in which all contributing entities or agents operate, communicate, and interact with each other productively. Then after, simulation and applicability implementation experiments on the basis of full-sized academic experimental platform have been conducted to validate the proposed control approach. Product and resource entities negotiate considering different key performance measures in order to set best priority-based product sequencing. This has been done with expectations that the applicability of the negotiation-based decision-making will be more adaptable to deal with perturbation(s) than another alternative decision-making approach called pure reactive control approach. The result showed that negotiation among the decisional entities has brought significant improvement in reducing makespan and hence conveyed better global performance of a manufacturing system.
DOI: 10.5683/sp3/4ayj45
title: An agent-based fuzzy constraint-directed negotiation model for solving supply chain planning and scheduling problems

description: Supply chain planning and scheduling problems require manufacturers (project managers) to determine product configurations, select suppliers (contractors) for task allocation, and schedule project tasks while considering various production constraints among manufacturers and suppliers. Most relevant studies have focused on finding an optimal solution based on complete information provided by each enterprise in a supply chain. However, the practical implementation of complete information sharing
is difficult, if not impossible, because of the fully distributed nature of the supply chain process. This work proposes an agent-based fuzzy constraint-directed negotiation (AFCN) model to solve problems associated with supply chain planning and scheduling, which are modeled as a set of fuzzy constraint satisfaction problems (FCSPs) that are interlinked via inter-agent constraints. To accommodate the perspectives and interests of each enterprise in a supply chain, conflicts among FCSPs are resolved using the AFCN protocol through the iterative exchange of offers/counter-offers with limited information sharing and without privacy breaches. A proposed offer/counter-offer represents not only a set of acceptable solutions and preferences for an operational task but also the possibility of conflict in this area. For each FCSP,the incremental process of offer/counter-offer evaluation eliminates redundant and infeasible solutions. The sharing of limited non-strategic sensitive information among agents enables them to elucidate their opponents’ intentions through iterative negotiations, such that the agents can reach an agreement while ensuring that the solution to a project planning and scheduling problem is satisfactory. The AFCN model is also sufficiently flexible to incorporate different negotiation strategies such as competitive, winwin, and collaborative strategies, for various production environments. Herein, a numerical study was conducted to examine the practical viability and effectiveness of the proposed AFCN model. The experimental results show that the proposed AFCN model not only can generate a schedule that is comparable to a near-optimal solution but also is time-efficient. This indicates that the proposed AFCN is a practical and effective method for solving supply chain planning and scheduling problems in fully distributed
environments.
DOI: 10.5683/sp3/bxuiji
title: Negotiation-based scheduling considering agent emotion

description: Scheduling in decentralized manufacturing systems is being expanded and transformed to a smart level where manufacturing tasks are executed autonomously. However, achieving self-organized manufacturing resources that can make collaborative decisions remains challenging. This paper presents a novel negotiation model for decentralized scheduling in an autonomous manufacturing system. A variety of emotions represent the willingness of autonomous intelligent agents (IAs) to negotiate for a consensus despite their diverse goals. The proposed communication protocol considers the emotional aspects of the autonomous machine agents in
decentralized decision-making. The negotiation model focuses on analyzing the effects of two key emotions: selfishness and cooperation. This study constitutes the basis for experiments on multi-goal machine tasks and artificial emotion mechanism in a flow shop production system. The complexities of negotiation dynamics between IAs, where the interplay of selfish and cooperative strategies influences the outcomes and efficiency of reaching agreements, are explored. The experimental results show that selfish IAs eventually reach agreements through gradual compromise, but cooperative IAs, with their flexibility, can quickly achieve resolutions, especially when bargaining power is balanced. Aligning with the dual concern theory, this study reveals that while
selfish IAs can dominate cooperative ones, the cooperative approach often leads to faster and more effective outcomes, regardless of the level of selfishness involved.
DOI: 10.5683/sp3/1m7f98
title: Strategic agents for multi-resource negotiation

description: In electronic commerce markets where selfish agents behave individually, agents often have to acquire multiple resources in order to accomplish a high level task with each resource acquisition requiring negotiations with multiple resource providers. Thus, it is cru- cial to efficiently coordinate these interrelated negotiations. This paper presents the design and implementation of agents that concurrently negotiate with other entities for acquiring multiple resources. Negotiation agents in this paper are designed to adjust (1) the number of tentative agreements for each resource and (2) the amount of concession they are will- ing to make in response to changing market conditions and negotiation situations. In our approach, agents utilize a time-dependent negotiation strategy in which the reserve price of each resource is dynamically determined by (1) the likelihood that negotiation will not be suc- cessfully completed (conflict probability), (2) the expected agreement price of the resource, and (3) the expected number of final agreements. The negotiation deadline of each resource is determined by its relative scarcity. Agents are permitted to decommit from agreements by paying a time-dependent penalty, and a buyer can make more than one tentative agreement for each resource. The maximum number of tentative agreements for each resource made by an agent is constrained by the market situation. Experimental results show that our negotiation strategy achieved significantly more utilities than simpler strategies.
DOI: 10.5683/sp3/fqwxh9
title: Design of automated negotiation mechanisms for decentralized heterogeneous machine scheduling

description: We consider a hard decentralized scheduling problem with heterogeneous machines and competing job sets that belong to different self-interested stakeholders (agents). The determination of a beneficial solution, i.e., a respective contract in terms of a common schedule, is particularly difficult due to information asymmetry
and self-interested behavior of the involved agents. The agents intend to minimize their individual costs that consist of tardiness cost and their share of the machine operating cost. The aim of this study is to find socially beneficial outcomes by means of negotiation mechanisms that comply with decentralized information
and conflicting interests. For this purpose, we present an automated negotiation protocol, which is inspired by metaheuristics, along with a set of optional building blocks. In the protocol, new solutions are iteratively generated, as mutations of a single provisional contract, and proposed to the agents, while feasible rules with quotas restrict the acceptance decisions of the agents. The computational experiments show that the protocol—without central information and subject to strategic behavior—can achieve high quality solutions which are very close to results from centralized multi-criteria procedures. Particular building block configurations yield improved outcomes. Concluding, the considered scheduling problem enhances standard scheduling models by incorporating multiple stakeholders, nonlinear cost functions, and machine operating cost, whereas the presented negotiation approach contributes to the methodology and practice of collaborative decision making.
DOI: 10.5683/sp3/9wb4uw
title: A Negotiation Protocol for Meeting Scheduling Agent

description: Negotiation is a general mechanism for reaching an agreement that involves multiple individuals. In multi-agent systems, automatic negotiation is one of the main ongoing research issues. Over the last two decades, many attempts have been made to handle naturally distributed agreement problems via automatic negotiation. These naturally distributed problems are easy to understand for their simplicity, but are hard to handle automatically. In order to reach an agreement using automatic negotiation, there is a need for a structured negotiation protocol. In this paper, we propose an agent
negotiation protocol for meeting scheduling, one of the prominent naturally distributed problem. This paper assumes that there is a scheduling agent and it has the knowledge about user preferences, meeting participants profile, holds a reasoning mechanism to evaluate a meeting invitation, and capable of selecting negotiation strategies automatically. The proposed negotiation protocol assists the meeting scheduling agent to handle bilateral and multilateral negotia- tion scenarios. We demonstrate a number of meeting scheduling scenarios to show how the protocol assists automatic negotiation process and its effectiveness during the scheduling activities.
DOI: 10.5683/sp3/dqyutq
title: Multi-agent negotiation based on price schedules algorithm for distributed collaborative design

description: Collaborative design is a common practice in modern product development. Companies specializing in different disciplines, which are often geographically separated, work together to develop solutions for the benefit of overall design. In such inter-organizational collaboration, participants hiding individual skills and design rationales is highly desirable. To fulfill this requirement, this study proposes a
negotiation mechanism based on a price schedules decomposition algorithm originally developed in economics. The mechanism searches for globally optimal designs, where no participant is necessary to own full knowledge of the entire design space. This paper also applies multi-agent system technologies to realize a secure environment for automating distributed collaborative design. A test scenario of distributed tolerance allocation in assembly design validates the proposed mechanism
DOI: 10.5683/sp3/guf2p5
title: Human-Agent Negotiations: The Impact of Agents’ Concession Schedule and Task Complexity on Agreements

description: Employment of software agents for conducting negotiations with online customers promises to increase the flexibility and reach of the exchange mechanism and reduce transaction costs. Past research had suggested different negotiation tactics for the agents, and had used them in experimental settings against human negotiators. This work explores the interaction between negotiation strategies and the complexity of the negotiation task as represented by the number of negotiation issues. Including more issues in a negotiation potentially allows the parties more space to maneuver and, thus, promises higher likelihood of agreement. In practice, the consideration of more is-sues requires higher cognitive effort, which could have a negative effect on reaching an agreement. The results of humanagent negotiation experiments conducted at a major Canadian university revealed that there is an interaction between chosen strategy and task complexity. Also, when competitive strategy was employed, the agents' utility was the highest. Because competitive strategy resulted in fewer agreements the average utility per agent was the highest in the compromising–competitive strategy.
DOI: 10.5683/sp3/ggcizq
title: Negotiation in Multi-Agent System using Partial-Order Schedule

description: In systems composed of multiple autonomous agents,
negotiation is a key form of interaction that enables groups
of agents to arrive at a mutual agreement regarding some
belief, goal or plan, for example. In general, multi-linked
negotiation (including both the directly linked and the
indirectly linked relationships) describes situations where
one agent needs to negotiate with multiple agents about
different issues, where the negotiation over one issue
influences the negotiations over other issues. The
characteristics of the commitment on one issue affect the
evaluation of a commitment or the construction of a
proposal for another issue. In this paper we built a partial
order schedule representation with the help of which we
effectively manage interacting negotiation issues.Also we
explored how flexibility is an important factor for ordering
and managing negotiation issues in a successful negotiation
and enables an agent to reason explicitly about the
interactions among multiple negotiation issues in order to
achieve higher performance.
DOI: 10.5683/sp3/iw4yuf
title: Coordinating patient preferences through automated negotiation: A multiagent systems model for diagnostic services scheduling

description: This paper presents a multiagent systems model for patient diagnostic services scheduling. We assume a decentralized environment in which patients are modeled as self-interested agents who behave strategically to advance their own benefits rather than the system wide performance. The objective is to improve the utilization
of diagnostic imaging resources by coordinating patient individual preferences through automated negotiation. The negotiation process consists of two stages, namely patient selection and preference scheduling. The contractnet protocol and simulated annealing based meta-heuristics are used to design negotiation protocols at the two
stages respectively. In terms of game theoretic properties, we show that the proposed protocols are individually rational and incentive compatible. The performance of the preference scheduling protocol is evaluated by a computational study. The average percentage gap analysis of various configurations of the protocol shows that
the results obtained from the protocol are close to the optimal ones. In addition, we present the algorithmic properties of the preference scheduling protocol through the validation of a set of eight hypotheses.
DOI: 10.5683/sp3/ecdn64
title: HRMS data for nontarget analysis

description: High resolution mass spectrometry data to support a nontarget analysis.
DOI: 10.5683/sp3/acsocd
title: A Hybrid Multiagent-Based Rescheduling Mechanism for Open and Stochastic Environments Concerning the Execution Stage

description: Dynamic scheduling is a hot topic in recent years. Most existing dynamic scheduling algorithms process the rescheduling in an off-line way through the modification of created schedules in advance of further execution. However, in open and stochastic environments, such as manufacturing systems, streams of exogenous events may cause information changes inside of jobs and resources during the job execution stage, so as to increase the environmental uncertainties and rescheduling computational complexity. In such environments, the frequent occurrence of dynamic events requires scheduling methods to provide efficient and robust online rescheduling services for ensuring the success rate of jobs. This paper investigates the dynamic events during the execution stage in open and stochastic environments and proposes a multiagent-based rescheduling mechanism with the hybrid structure for such cases. Three types of agents are proposed to implement the rescheduling mechanism. The experimental results demonstrate that the proposed hybrid mechanism own higher efficiency and robustness in dealing with dynamic events in open and stochastic scheduling environments than that of existing centralized and decentralized methods.
DOI: 10.5683/sp3/etoqxs
title: Agent-based simulation and optimization of hybrid flow shop considering multi-skilled workers and fatigue factors

description: •Worker fatigue and skill levels are considered in the modeling of hybrid flow shop.
•Parameters of the fatigue model are considered as uncertain factors.
•Agent-based simulation is combined reinforcement learning and meta-heuristics.
•A simulation-based optimization framework is developed.