Minyi Li

Automated negotiation in open and distributed environments

Automated negotiation is one of the most common approaches used to make decisions and manage disputes between computational entities leading them to optimal agreements. Many existing works tackle single-issue negotiations and the negotiation environment is assumed to be static so that the agents can make decisions based solely on the proposals of the counterparts and their own fixed parameters. Most real-world scenarios, however, involve complex domains and dynamic environments. In such cases, it is no longer sufficient to consider negotiation as an isolated activity in a static environment. Therefore, a more general framework for automated negotiation is needed in which the negotiation agents can be very flexible and adaptive. In this paper, we describe a generic framework for automated negotiation, which captures descriptively the social dynamics of the negotiation process. The proposed framework enables the agents to behave responsively to the changes in the environment. Their strategies can adapt as the conditions outside of the negotiation change to ensure that their decisions remain rational. And the agents are proactive and responsive by searching for options, which are outside of the negotiation and which may improve their outcomes. The key ideas and the overall system architecture together with a specific negotiation instance in a basic bilateral setting are described, along with two illustrative examples. The first example is in the context of e-commerce, and the second example is an application scenario of service level agreement negotiation in service computing. We also describe a prototypical implementation of the proposed negotiation framework.

Shortest paths in stochastic time-dependent networks with link travel time correlation

This paper develops a simple, robust framework for the problem of finding the route with the least expected travel time from any node to any given destination in a stochastic and time-dependent network. Spatial and temporal link travel time correlations are both considered in the proposed solution, which is based on a dynamic programming approach. In particular, the spatial correlation is represented by a Markovian property of the link states, in which each link is assumed to experience congested or uncongested conditions. The temporal correlation is manifested through the time-dependent expected link travel time given the condition of the link traversed. The framework enables the use of a route guidance system, in which at any decision node within a network, a decision can be made on the basis of current traffic information about which node to take next to achieve the shortest expected travel time to the destination. Numerical examples are presented to illustrate the computational steps involved in the framework to make route choices and to demonstrate the effectiveness of the proposed solution.

Reliability in stochastic time-dependent traffic networks with correlated link travel times

Optimal route selection with reliable expected travel time has been a focus of research in transportation networks where the reliability is subject to many uncertainty factors such as traffic incidents or recurring traffic congestions. In this paper we develop an approximation method to obtain the reliability of a route travel time in a stochastic time-dependent traffic network. The proposed method takes into consideration the probabilistic nature of the travel time on individual links and their spatial correlation between adjacent links of a selected route. Reliability calculations and the accuracy of our approximation are discussed via a simple illustrative example and a larger network.

Aggregating multi-valued CP-nets: a CSP-based approach

We develop a framework for preference aggregation in multi-attribute, multi-valued domains, where agents’ preferences are represented by Conditional Preference Networks (CP-nets). Most existing work either does not consider computational requirements, or depends on the strong assumption that the agents can express their preferences by acyclic CP-nets that are compatible with a common order on the variables. In this paper, we focus on majoritarian aggregation of CP-nets. We propose a general approach that allows for aggregating preferences when the expressed CP-nets are not required to be acyclic. Moreover, there is no requirement for any common structure among the agents’ CP-nets. The proposed approach computes a set of locally winning alternatives through the reduction to a constraint satisfaction problem. We present results of experiments that demonstrate the efficiency and scalability of our approach. Through comprehensive experiments we also investigate the distributions of the numbers of locally winning alternatives with different CP-net structures, with varying domain sizes and varying numbers of variables and agents.

A distributed social choice protocol for combinatorial domains

In this paper, we study the problem of collective decision-making over combinatorial domains, where the set of possible alternatives is a Cartesian product of (finite) domain values for each of a given set of variables, and these variables are not preferentially independent. Due to the large alternative space, most common rules for social choice cannot be directly applied to compute a winner. In this paper, we introduce a distributed protocol for collective decision-making in combinatorial domains, which enjoys the following desirable properties: (i) the final decision chosen is guaranteed to be a Smith member; (ii) it enables distributed decision-making and works under incomplete information settings, i.e., the agents are not required to reveal their preferences explicitly; (iii) it significantly reduces the amount of dominance testings (individual outcome comparisons) that each agent needs to conduct, as well as the number of pairwise comparisons; (iv) it is sufficiently general and does not restrict the choice of preference representation languages.

An Efficient Approach for Ordering Outcomes and Making Social Choices with CP-Nets

In classical decision theory, the agents’ preferences are typically modelled with utility functions that form the base for individual and multi-agent decision-making. However, utility-based preference elicitation is often complicated and sometimes not so user-friendly. In this paper, we investigate the theory of CP-nets (conditional preference networks) as a formal model for representing and reasoning with the agents’ preferences. The contribution of this paper is two-fold. First, we propose a tool, called RA-Tree (Relational Assignment Tree), to generate the preference order over the outcome space for an individual agent. Moreover, when multiple agents interact, there is a need to make social choices. But given a large number of possible alternatives, it is impractical to search the collective optimal outcomes from the entire outcome space. Thus, in this paper, we provide a novel procedure to generate the optimal outcome set for multiple agents. The proposed procedure reduces the size of the search space and is computationally efficient.

Choosing combinatorial social choice by heuristic search

This paper studies the problem of computing aggregation rules in combinatorial domains, where the set of possible alternatives is a Cartesian product of (finite) domain values for each of a given set of variables, and these variables are usually not preferentially independent. We propose a very general heuristic framework SC* for computing different aggregation rules, including rules for cardinal preference structures and Condorcet-consistent rules. SC* highly reduces the search effort and avoid many pairwise comparisons, and thus it significantly reduces the running time. Moreover, SC* guarantees to choose the set of winners in aggregation rules for cardinal preferences. With Condorcet-consistent rules, SC* chooses the outcomes that are sufficiently close to the winners.

QoS- and resource-aware service composition and adaptation

Flexible and adaptive quality-of-service (QoS) is desirable for distributed real-time applications, such as e-commerce, or multimedia applications. The objective of this research is to dynamically instantiate composite services by effectively utilising the collective capabilities of the resources to deliver distributed applications. Related to this objective are the problems of: (1) predicting system and network resources utilisation as well as the users changing requirements on the provided services, and (2) finding optimal execution plans for a service that meet end-to-end quality requirements and adapting the available resources in accordance to the changing situation. This paper presents a framework for adaptive QoS and resource management in provisioning composite services. We also develop distributed algorithms for finding the multi-constrained optimal execution plan to enable delivery of QoS-assured composite services.