The importance of context-dependent learning in negotiation agents

Automated negotiation between arti cial agents is essential to deploy Cognitive Computing and Internet of Things. The behavior of a negotiating agent depends signi cantly on the in uence of environmental conditions or contextual variables, since they affect not only a given agent preferences and strategies, but also those of other agents. Despite this, the existing literature on automated negotiation is scarce about how to properly account for the effect of context-relevant variables in learning and evolving strategies. In this paper, a novel context-driven representation for automated negotiation is proposed. Also, a simple negotiating agent that queries available information from its environment, internally models contextual variables, and learns how to take advantage of this knowledge by playing against himself using reinforcement learning is proposed. Through a set of episodes against negotiating agents in the existing literature, it is shown that it makes no sense to negotiate without taking context-relevant variables into account. The context-aware negotiating agent has been implemented in the GENIUS negotiation environment, and results obtained are signi cant and revealing.Sociedad Argentina de Informática e Investigación Operativ

The importance of context-dependent learning in negotiation agents

Automated negotiation between arti cial agents is essential to deploy Cognitive Computing and Internet of Things. The behavior of a negotiating agent depends signi cantly on the in uence of environmental conditions or contextual variables, since they affect not only a given agent preferences and strategies, but also those of other agents. Despite this, the existing literature on automated negotiation is scarce about how to properly account for the effect of context-relevant variables in learning and evolving strategies. In this paper, a novel context-driven representation for automated negotiation is proposed. Also, a simple negotiating agent that queries available information from its environment, internally models contextual variables, and learns how to take advantage of this knowledge by playing against himself using reinforcement learning is proposed. Through a set of episodes against negotiating agents in the existing literature, it is shown that it makes no sense to negotiate without taking context-relevant variables into account. The context-aware negotiating agent has been implemented in the GENIUS negotiation environment, and results obtained are signi cant and revealing.Sociedad Argentina de Informática e Investigación Operativ

The importance of context-dependent learning in negotiation agents

Automated negotiation between arti cial agents is essential to deploy Cognitive Computing and Internet of Things. The behavior of a negotiating agent depends signi cantly on the in uence of environmental conditions or contextual variables, since they affect not only a given agent preferences and strategies, but also those of other agents. Despite this, the existing literature on automated negotiation is scarce about how to properly account for the effect of context-relevant variables in learning and evolving strategies. In this paper, a novel context-driven representation for automated negotiation is proposed. Also, a simple negotiating agent that queries available information from its environment, internally models contextual variables, and learns how to take advantage of this knowledge by playing against himself using reinforcement learning is proposed. Through a set of episodes against negotiating agents in the existing literature, it is shown that it makes no sense to negotiate without taking context-relevant variables into account. The context-aware negotiating agent has been implemented in the GENIUS negotiation environment, and results obtained are signi cant and revealing.Sociedad Argentina de Informática e Investigación Operativ

The importance of context-dependent learning in negotiation agents

Automated negotiation between artificial agents is essential to deploy Cognitive Computing and Internet of Things. The behavior of a negotiation agent depends significantly on the influence of environmental conditions or contextual variables, since they affect not only a given agent preferences and strategies, but also those of other agents. Despite this, the existing literature on automated negotiation is scarce about how to properly account for the effect of context-relevant variables in learning and evolving strategies. In this paper, a novel context-driven representation for automated negotiation is introduced. Also, a simple negotiation agent that queries available information from its environment, internally models contextual variables, and learns how to take advantage of this knowledge by playing against himself using reinforcement learning is proposed. Through a set of episodes against other negotiation agents in the existing literature, it is shown using our context-aware agent that it makes no sense to negotiate without taking context-relevant variables into account. Our context-aware negotiation agent has been implemented in the GENIUS environment, and results obtained are significant and quite revealing

An Evolutionary Learning Approach for Adaptive Negotiation Agents

Developing effective and efficient negotiation mechanisms for real-world applications such as e-Business is challenging since negotiations in such a context are characterised by combinatorially complex negotiation spaces, tough deadlines, very limited information about the opponents, and volatile negotiator preferences. Accordingly, practical negotiation systems should be empowered by effective learning mechanisms to acquire dynamic domain knowledge from the possibly changing negotiation contexts. This paper illustrates our adaptive negotiation agents which are underpinned by robust evolutionary learning mechanisms to deal with complex and dynamic negotiation contexts. Our experimental results show that GA-based adaptive negotiation agents outperform a theoretically optimal negotiation mechanism which guarantees Pareto optimal. Our research work opens the door to the development of practical negotiation systems for real-world applications

Implementing a Business Process Management System Using ADEPT: A Real-World Case Study

This article describes how the agent-based design of ADEPT (advanced decision environment for processed tasks) and implementation philosophy was used to prototype a business process management system for a real-world application. The application illustrated is based on the British Telecom (BT) business process of providing a quote to a customer for installing a network to deliver a specified type of telecommunication service. Particular emphasis is placed upon the techniques developed for specifying services, allowing heterogeneous information models to interoperate, allowing rich and flexible interagent negotiation to occur, and on the issues related to interfacing agent-based systems and humans. This article builds upon the companion article (Applied Artificial Intelligence Vol.14, no 2, pgs. 145-189) that provides details of the rationale and design of the ADEPT technology deployed in this application

Acceptance conditions in automated negotiation

In every negotiation with a deadline, one of the negotiating parties has to accept an offer to avoid a break off. A break off is usually an undesirable outcome for both parties, therefore it is important that a negotiator employs a proficient mechanism to decide under which conditions to accept. When designing such conditions one is faced with the acceptance dilemma: accepting the current offer may be suboptimal, as better offers may still be presented. On the other hand, accepting too late may prevent an agreement from being reached, resulting in a break off with no gain for either party. Motivated by the challenges of bilateral negotiations between automated agents and by the results and insights of the automated negotiating agents competition (ANAC), we classify and compare state-of-the-art generic acceptance conditions. We focus on decoupled acceptance conditions, i.e. conditions that do not depend on the bidding strategy that is used. We performed extensive experiments to compare the performance of acceptance conditions in combination with a broad range of bidding strategies and negotiation domains. Furthermore we propose new acceptance conditions and we demonstrate that they outperform the other conditions that we study. In particular, it is shown that they outperform the standard acceptance condition of comparing the current offer with the offer the agent is ready to send out. We also provide insight in to why some conditions work better than others and investigate correlations between the properties of the negotiation environment and the efficacy of acceptance condition

Risk assessment and relationship management: practical approach to supply chain risk management

The literature suggests the need for incorporating the risk construct into the measurement of organisational performance, although few examples are available as to how this might be undertaken in relation to supply chains. A conceptual framework for the development of performance and risk management within the supply chain is evolved from the literature and empirical evidence. The twin levels of dyadic performance/risk management and the management of a portfolio of performance/risks is addressed, employing Agency Theory to guide the analysis. The empirical evidence relates to the downstream management of dealerships by a large multinational organisation. Propositions are derived from the analysis relating to the issues and mechanisms that may be employed to effectively manage a portfolio of supply chain performance and risks