Khalid Mansour

A Meta-Strategy for Coordinating of One-to-Many Negotiation over Multiple Issues

This paper presents a novel approach for managing multi-bilateral concurrent negotiations. We extend our previous work by considering a situation where a buyer agent negotiates with multiple seller agents concurrently over multiple continuous issues instead of a single issue. A related work in this area considers a meta-strategy for bilateral negotiations. This work adapts the previous related work to coordinate multi-bilateral concurrent negotiations taking into consideration the different behaviors of the opponents during negotiation to decide on choosing the appropriate negotiation strategy (i.e., trade-off or concession) for the buyer agent’s delegates at each negotiation round. A negotiation meta-strategy to coordinate the one-to-many negotiation form is proposed and empirically tested under various negotiation environments. The experiments show the robustness of our coordination mechanism.

Real-Time Coordination of Concurrent Multiple Bilateral Negotiations under Time Constraints

This paper addresses a coordination problem during concurrent multiple bilateral negotiations. Real time decisions need to be taken during negotiation to ensure that the overall negotiation process is successful in terms of achieving a valuable agreement (an agreement with high utility). Existing approaches for managing concurrent multiple bilateral negotiation typically rely on historic information such as the probability distribution of the opponents’ reservation prices, deadlines, or the results of the previous negotiations. This paper presents a novel heuristic coordination mechanism for coordinating concurrent multiple bilateral negotiations assuming no previous knowledge about the opponents. The proposed method uses two measures to evaluate the relative behavior of each opponent. The first measure uses the first-order differences of the current negotiation offers and calculates the relative concession for each opponent agent that depends on the mutual concessions of all agents involved in negotiation. The second measure uses a scoring function to evaluate each of the seller’s last offer during negotiation. We use both measures to change the negotiation strategy during negotiation. The empirical results show that our model is more effective when compared with some existing approaches.

On Dynamic Negotiation Strategy for Concurrent Negotiation over Distinct Objects

This paper addresses the problem of generating counteroffers by a buyer agent negotiating with multiple seller agents concurrently over multiple distinct negotiation objects. Each object has one provider and characterized by multiple issues, i.e., attributes. Most previous works address negotiation strategies for simpler situations where an agent negotiates with multiple opponents for the purpose of securing an agreement over a single object with either a single negotiation issue or multiple negotiation issues. We propose a novel dynamic negotiation strategy that works in a more complicated negotiation scenario. The strategy involves adaptation of both the initially generated counteroffers and the issues’ counteroffers weight matrix during negotiation. The proposed dynamic strategy takes into consideration the behaviors of the current opponents in terms of their recent concessions to fine-tune the negotiation strategy of the agent in real-time. The initial experimental results show that the proposed mechanism is effective in terms of both the agreement and the utility rates when compared with a static strategy.

Concurrent Negotiation over Quality of Service

This paper addresses the problem of flexible procurement of multiple services with multiple non-functional characteristics, i.e., quality of service attributes. We consider the one-to-many negotiation approach as a flexible method for procuring multiple services by a buyer agent. We address the problem of coordinating multiple concurrent negotiations and propose a novel dynamic negotiation strategy that considers the behaviors of the opponents of the current negotiation encounter in managing the local reservation values of the common negotiation issues (attributes) of different services. Most previous works consider the problem of negotiation over a single issue. We investigate a more complex situation where a buyer agent negotiates over multiple services given that each service has multiple negotiation issues. The experimental results show an evidence for the effectiveness and robustness of our dynamic negotiation strategy in various negotiation environments.

Coordinating the Bidding Strategy in Multiissue Multiobject Negotiation With Single and Multiple Providers

This paper addresses the problem of flexible procurement of multiple distinct services characterized by multiple nonfunctional characteristics, i.e., quality-of-service attributes. We consider the one-to-many negotiation approach as a flexible method for procuring multiple different services by a buyer agent. We address the problem of coordinating the bidding strategy amongst multiple concurrent negotiations and propose novel dynamic negotiation strategies. The proposed strategies consider the behaviors of the opponents of the current negotiation encounter in managing the local reservation values of the common negotiation issues (attributes) of different services. Most previous works consider the problem of negotiation over a single object characterized by one or more issues. We extend our previous work and investigate a more complex situation where a buyer agent negotiates over multiple distinct services given that each service has multiple negotiation issues and multiple possible providers. The experimental results show evidence for the effectiveness and robustness of our dynamic negotiation strategies under various negotiation environments.

On Effective Quality of Service Negotiation

This paper addresses the problem of flexible procuring of multiple services with multiple non-functional characteristics, i.e., quality of service attributes. We investigate the one-to-many negotiation approach as a flexible method for procuring multiple services by a buyer agent. We address the problem of coordinating multiple concurrent negotiations and propose a novel dynamic negotiation strategy that considers the behaviors of the opponents in managing the local reservation values for the common negotiation issues. Most previous works consider the problem of negotiation over a single issue. We investigate a more complex scenario where a buyer agent negotiates with multiple seller agents over multiple services characterized by multiple issues. The initial experimental results show the effectiveness of our dynamic negotiation strategy when compared to a static strategy.

Scheduling for Optimal Response Times in Queues of Stochastic Workflows

We investigate the problem of scheduling tasks of structured workflows, given a stochastic arrival of workflow instances, which gives rise to a queue. Each workflow conforms to a known structure expressed by a directed acyclic graph. However, within this model, the precise execution time of each atomic task and the delay of each communication edge are non-deterministic. Unlike in most scheduling approaches that minimize the schedule length, we additionally aim at minimizing the total time spent by a workflow instance in the system, as perceived by the end user on whose behalf the workflow is executed, i.e., the expected response time. Moreover, we do not make any restrictive assumptions on the nature of the involved distributions. We propose a novel risk-gain local trade-off mechanism to determine priorities at runtime that optionally can be made even more accurate by employing of conditional means for running activities instead of marginal mean execution times. Finally, the tasks that are unlikely to affect the makespan of an instance are delayed with a local look-ahead to allow incoming new instances to start earlier. We show that adding these features leads to a significant improvement in response time, particularly in situations of scarce processing resources.

Managing concurrent negotiations in multi-agent systems

The one-to-many agent system is a typical multi-agent system that involves interaction between agents through negotiation. The one-to-many negotiation form is a complicated problem especially when the negotiation is about distinct negotiation objects characterized by multiple negotiation issues. The complexity of the problem comes from the existence of many variables in the negotiation process. For example, the number of agents, the number of objects and the number of negotiation issues contribute to the problem complexity. Few existing works address some aspects of the coordination problem in the one-to-many negotiation form. However, most works address simple negotiation scenarios such as negotiation over a single object characterized by a single issue or multiple issues. The aim of this research is to investigate possible coordination problems in the one-to-many negotiation form and propose effective and robust solutions for a number negotiation scenarios. We test our solutions by evaluating some negotiation objective criteria such as utility gain, agreement rate etc.

Aspects of Coordinating the Bidding Strategy in Concurrent One-to-Many Negotiation

Automated negotiation is an important mechanism of interaction between software agents and has been an active research area for more than a decade. When the automated negotiation process involves multiple agents, the problem of interdependency between the actions of agents during negotiation arises and consequently, a coordination mechanism becomes an essential part of the negotiation process. One of the important characteristics of a negotiating agent is its bidding strategy. This work addresses the problem of coordinating the bidding strategy of an agent negotiating concurrently with multiple agents (i.e., one-to-many negotiation) and discusses different interdependency factors affecting it.

Resource allocation for multiple cloud workflows under execution time uncertainty

We investigate the problem of scheduling tasks in a cloud computing scenario that involves a queue of workflow instances whose arrival into the system is governed by a stochastic process. We assume that each instance conforms to the same, known structure, expressed by a directed acyclic graph. Within this job model, the precise execution time of each atomic task and delay of each communication edge may be different and is known only in terms of probability distributions. We argue that in many serial information processing scenarios, minimizing the total response time of the system, i.e., the sum of the execution time and in-queue waiting time, is a more important objective than reducing the schedule length alone, as predominantly done in other approaches. We propose novel algorithms for the minimization of the expected response time and investigate the issue of estimation of the stability of the system. We show that for the given objective, greedy algorithms usually perform better at lower instance arrival rates or when the total amount of instances is limited. However, we also show that an algorithm that minimizes only the expected service time of an instance makes the system more stable and that it must be a non-greedy algorithm.