Learning about the opponent in automated bilateral negotiation: a comprehensive survey of opponent modeling techniques

A negotiation between agents is typically an incomplete information game, where the agents initially do not know their opponent’s preferences or strategy. This poses a challenge, as efficient and effective negotiation requires the bidding agent to take the other’s wishes and future behavior into account when deciding on a proposal. Therefore, in order to reach better and earlier agreements, an agent can apply learning techniques to construct a model of the opponent. There is a mature body of research in negotiation that focuses on modeling the opponent, but there exists no recent survey of commonly used opponent modeling techniques. This work aims to advance and integrate knowledge of the field by providing a comprehensive survey of currently existing opponent models in a bilateral negotiation setting. We discuss all possible ways opponent modeling has been used to benefit agents so far, and we introduce a taxonomy of currently existing opponent models based on their underlying learning techniques. We also present techniques to measure the success of opponent models and provide guidelines for deciding on the appropriate performance measures for every opponent model type in our taxonomy.     

Design techniques for the definition of solid objects with free-form geometry

Solid modellers and free-form surface systems have been evolving to a large extent separately for fifteen years or more. Free-form surface techniques must now be integrated into solid modelling systems, since many engineering parts involve this type of geometry. The present paper classifies free-form surfaces into four categories for design purposes, depending on the degree of geometric constraint involved in their definition. Proposals are then made for free-form design techniques suitable for use in solid modellers. A survey of existing solid modelling systems is included, with indications of the techniques currently available for the design of free-form solids.     

Consistency preserving co-evolution of formal specifications and agent-oriented conceptual models

Many modelling techniques tend to address “late-phase” requirements while many critical modelling decisions (such as determining the main goals of the system, how the stakeholders depend on each other, and what alternatives exist) are taken during early-phase requirements engineering. The i¹ modelling framework is a semiformal agent-oriented conceptual modelling language that is well-suited for answering these questions. This paper addresses key challenge faced in the practical deployment of agent-oriented conceptual modelling frameworks such as i¹. Our approach to addressing this problem is based on the observation that the value of conceptual modelling in the i¹ framework lies in its use as a notation complementary to existing requirements modelling and specification languages, i.e., the expressive power of i¹ complements rather than supplants that of existing notations. The use of i¹ in this fashion requires that we define methodologies that support the co-evolution of i¹ models with more traditional specifications. This research examines how this might be done with formal specification notations (specifically Z).     

Case-based analysis in user requirements modelling for knowledge construction

ContextLearning can be regarded as knowledge construction in which prior knowledge and experience serve as basis for the learners to expand their knowledge base. Such a process of knowledge construction has to take place continuously in order to enhance the learners’ competence in a competitive working environment. As the information consumers, the individual users demand personalised information provision which meets their own specific purposes, goals, and expectations.ObjectivesThe current methods in requirements engineering are capable of modelling the common user’s behaviour in the domain of knowledge construction. The users’ requirements can be represented as a case in the defined structure which can be reasoned to enable the requirements analysis. Such analysis needs to be enhanced so that personalised information provision can be tackled and modelled. However, there is a lack of suitable modelling methods to achieve this end. This paper presents a new ontological method for capturing individual user’s requirements and transforming the requirements onto personalised information provision specifications. Hence the right information can be provided to the right user for the right purpose.MethodAn experiment was conducted based on the qualitative method. A medium size of group of users participated to validate the method and its techniques, i.e. articulates, maps, configures, and learning content. The results were used as the feedback for the improvement.ResultThe research work has produced an ontology model with a set of techniques which support the functions for profiling user’s requirements, reasoning requirements patterns, generating workflow from norms, and formulating information provision specifications.ConclusionThe current requirements engineering approaches provide the methodical capability for developing solutions. Our research outcome, i.e. the ontology model with the techniques, can further enhance the RE approaches for modelling the individual user’s needs and discovering the user’s requirements.     

Analysis and classification of data sets for calibration and validation of agro-ecosystem models

Experimental field data are used at different levels of complexity to calibrate, validate and improve agro-ecosystem models to enhance their reliability for regional impact assessment. A methodological framework and software are presented to evaluate and classify data sets into four classes regarding their suitability for different modelling purposes. Weighting of inputs and variables for testing was set from the aspect of crop modelling. The software allows users to adjust weights according to their specific requirements. Background information is given for the variables with respect to their relevance for modelling and possible uncertainties. Examples are given for data sets of the different classes. The framework helps to assemble high quality data bases, to select data from data bases according to modellers requirements and gives guidelines to experimentalists for experimental design and decide on the most effective measurements to improve the usefulness of their data for modelling, statistical analysis and data assimilation.     

General-purpose software for intertemporal economic models

Intertemporal modelling is becoming increasingly important in general equilibrium policy analysis. Issues such as natural resource management, pollution control, investment, the promotion of technical change, and the accumulation of foreign debt all involve explicit intertemporal behavior and thus require intertemporal models to address adequately. Applied researchers have been slow to adopt the intertemporal paradigm because it can impose formidable computational requirements. In this paper we address this problem by presenting flexible, efficient new software which can solve complex intertemporal models in a fraction of the time required by conventional approaches.Our intertemporal software is an extension of GEMPACK, a suite of general-purpose computer programs developed to streamline the implementation and solution of a wide class of applied partial or general equilibrium models. This paper describes GEMPACK's new intertemporal modelling capabilities which make it straightforward for modellers to implement and solve their models. Because GEMPACK operates on models written in a syntax similar to ordinary algebra, it is easy for researchers to revise their models whenever necessary.In summary, this new software removes the computational impediments to intertemporal modelling, thereby allowing modellers to concentrate on the economics of their models.Industry Commission     

The development and application of CFD models for water treatment flocculators

The use of CFD to simulate turbulent flows in laboratory and full scale flocculation processes commonly found at water treatment works is reported. The paper considers a range of modelling strategies and simulation techniques including, inter alia, steady and unsteady flow, two-equation and Reynolds stress turbulence modelling, sliding mesh and multiple reference frames approaches to rotational flow simulation, and mesh density optimisation. Through analysis of turbulence dissipation rate, this work considers the development of models for environmental engineering problem solving and demonstrates clearly the benefits to be gained from the use of CFD in flocculation vessels.     

Modelling Trust in Artificial Agents, A First Step Toward the Analysis of e-Trust

This paper provides a new analysis of e-trust, trust occurring in digital contexts, among the artificial agents of a distributed artificial system. The analysis endorses a non-psychological approach and rests on a Kantian regulative ideal of a rational agent, able to choose the best option for itself, given a specific scenario and a goal to achieve. The paper first introduces e-trust describing its relevance for the contemporary society and then presents a new theoretical analysis of this phenomenon. The analysis first focuses on an agent’s trustworthiness, this one is presented as the necessary requirement for e-trust to occur. Then, a new definition of e-trust as a second-order-property of first-order relations is presented. It is shown that the second-order-property of e-trust has the effect of minimising an agent’s effort and commitment in the achievement of a given goal. On this basis, a method is provided for the objective assessment of the levels of e-trust occurring among the artificial agents of a distributed artificial system.     

Developing a Theory to Explain the Insights Gained Concerning Information Systems and Business Process Behaviour: The ASSESS-IT Project

Business Process (BP) literature promotes the value of business processes as essential gearwheels that help organisations to reach their goals. Similarly, many process design approaches claim that information technology is a major enabler of business process, a view also shared by the Information Systems (IS) community. Despite this, BP and IS approaches do not provide clear guidance on how to coordinate the design of BP and IS. Nor is any indication of which modelling techniques could be used to detect and design IT opportunities within a business process context given.The ASSESS-IT project examined this domain and proposed the use of simulation techniques to achieve BP and IT integration. The outcome of this project gave indication that depicting the dynamic behaviour of IS could be very helpful for BP modellers to predict the impact that the insertion of IS may have on organisational processes. This paper proposes a simulation framework to depict IS behaviour within a BP simulation model, namely BPS/ISS, and tests it using the case study that was part of the ASSESS-IT project.     

A DPS-based system modelling method for 3D-structures simulation in manufacturing processes

In the field of engineering, guidelines and computerisation are important to facilitate the resolution of complex problems. Engineers apply computer techniques of distributed problem solving (DPS) to design and simulation tasks, such as in industrial manufacture (e.g., simulation of aircraft, modelling of structures and mass customization). An important application of these and other computer-aided techniques is the design and manufacture of industrial greenhouses in south-eastern Spain. The importance of greenhouses in this region led to the establishment of European Standard UNE-EN 13031-1 for the design and construction of structures for commercial production. Computer-assisted techniques are helping to put this European Manufacturing Standard into effect. This article discusses how DPS techniques help to solve problems in industrial manufacturing processes, and presents the case of modelling and simulation of 3D-structures used in greenhouse construction.     

Model-based software configuration: patterns and languages

The common presupposition of enterprise systems (ES) is that they lead to significant efficiency gains. However, this is only the case for well-implemented ES that are well-aligned with the organisation. The list of ES implementation failures is significant which is partly attributable to the insufficiently addressed fundamental problem of adapting an ES efficiently. As long as it is not intuitively possible to configure an ES, this problem will prevail because organisations have a non-generic character. A solution to this problem consists in re-thinking current practices of ES provision. This paper proposes a new approach based on configurable process models, which reflect ES functionalities. We provide in this paper a taxonomy of situations that can occur from a business perspective during process model configuration. This taxonomy is represented via so-called semantic configuration patterns. In the next step, we discuss so-called syntactic configuration patterns. This second type of configuration patterns implements the semantic configuration patterns for specific modelling techniques. We chose two popular process modelling languages in order to illustrate our approach.     

TaxoPublish: Towards a solution to automatically personalize taxonomies in e-catalogs

Taxonomies are utilized in e-catalogs to facilitate customers navigating through a marketplace with the help of hierarchically structured concepts. However, when entering the e-catalog, each customer is shown the identical taxonomy regardless their individual requirements. Customers are distracted when navigating to preferred concepts as those are siblings of not required concepts. Provided progress in dynamic taxonomies, catalog segmentation, and personalized directories lacks in a fully automatic support for modifying the taxonomy according to the user’s requirements. The existing works need an explicit user-query, are missing information about the domain, or require the modification through the provider. In this paper, TaxoPublish expert system based on logic programming is presented. The proposed system predicts the customers requirements for automatically modifying the taxonomy in B2B context. With TaxoPublish, retailers can now provide personalization in the form of personalized e-catalogs without any human effort, and without missing any information about the domain. TaxoPublish is using knowledge provided through a Customer Relationship Management system for predicting customers preferences, and knowledge of a Product Information Management system for performing taxonomic operations based on two novel types of taxonomic concepts. Through the usage of logic programming and the cross-platform database model, TaxoPublish can be applied as expert system over distributed and heterogeneous data warehouse architectures across various domains. The comprehensive experiments on two public and one private database show that TaxoPublish expert system is capable of fully-automatic taxonomy modification with an accuracy similar to the expert manual modifications.     

Escape and intervention in multi-agent systems

This paper describes the escape/intervention concept as it is used in the agent growing environment framework. The Escape and Intervention is used in many multi-disciplinary areas, including agent research, artificial intelligence, groupware and workflow, process support, software engineering, and social sciences. Based on an ontological perspective, this paper explains how an interaction-oriented agent architecture and language (used for modelling, simulation, and development) makes use of an interaction pattern that is inspired from social contexts seen as multi-agent systems.     

A review of solid shape modelling based on integrity verification

The potential benefits of using a canonical model for representing the shapes of solid objects has led to the design and implementation of a number of geometric modellers, with varying capabilities. This paper reviews the approaches taken in solid modelling by defining the well-formedness conditions which must be implemented in any modelling system. The methods for satisfying the well-formedness constraints in the various solid modelling methods are reviewed, using the concept of integrity constraint. The incorporation of integrity constraints, both explicitly and implicitly, into solid modellers are considered, with particular focus on boundary modellers. The use of integrity constraints for defining shape families and assembly families are also presented. The result is a unified view of solid shape modelling systems that enables their classification and extension into particular application areas.     

Modelling pressure drop in water treatment

Industrial concern regarding the use of ‘black box’ models has understandably limited their routine application. In an attempt to overcome some of these concerns the technique of hybrid modelling (Thompson M. L. and Kramer, M. A., AIChE Journal, 1991, 40, 1328–1340) has become a popular alternative. It has been applied to many different processes and has shown a marked improvement on the now more traditional data-based models such as artificial neural networks (Psichogios D. C. and Ungar L. H., AIChE Journal, 1992, 38, 1499–1511) and NARMAX (Billings S. A., Gray J. O. and Owens D. H., Nonlinear Systems Design. Peter Peregrinus Ltd, 1984) structures. Hybrid models consist of a ‘black box’ model combined with a model of predefined structure (often mechanistic/first principles model) thereby gaining the advantages of both modelling techniques, namely the non-linear capabilities of a generally structured neural network and the extrapolation capabilities of a more rigid model.In this paper the ability of two different hybrid modelling techniques, combining an artificial neural network with a model of appropriate structure, to predict loss of head of pressure profiles in a water treatment plant, are compared. Data were obtained at 15-min intervals from on-line plant sensors. The input variables used were incoming water flowrate, raw water turbidity, supernatant return flowrate, pH of water entering the filters and solids build up on the filters. Results show the serial structure to be more robust allowing extrapolation outside the range of the training data set as compared to the parallel arrangement yielding a 20% reduction in RMS validation error.     

The role of information structures in game-theoretic multi-agent learning

Multi-agent learning (MAL) studies how agents learn to behave optimally and adaptively from their experience when interacting with other agents in dynamic environments. The outcome of a MAL process is jointly determined by all agents’ decision-making. Hence, each agent needs to think strategically about others’ sequential moves, when planning future actions. The strategic interactions among agents makes MAL go beyond the direct extension of single-agent learning to multiple agents. With the strategic thinking, each agent aims to build a subjective model of others decision-making using its observations. Such modeling is directly influenced by agents’ perception during the learning process, which is called the information structure of the agent’s learning. As it determines the input to MAL processes, information structures play a significant role in the learning mechanisms of the agents. This review creates a taxonomy of MAL and establishes a unified and systematic way to understand MAL from the perspective of information structures. We define three fundamental components of MAL: the information structure (i.e., what the agent can observe), the belief generation (i.e., how the agent forms a belief about others based on the observations), as well as the policy generation (i.e., how the agent generates its policy based on its belief). In addition, this taxonomy enables the classification of a wide range of state-of-the-art algorithms into four categories based on the belief-generation mechanisms of the opponents, including stationary, conjectured, calibrated, and sophisticated opponents. We introduce Value of Information (VoI) as a metric to quantify the impact of different information structures on MAL. Finally, we discuss the strengths and limitations of algorithms from different categories and point to promising avenues of future research.     

Improving component selection and monitoring with controlled experimentation and automated measurements

Context: A number of approaches have been proposed for the general problem of software component evaluation and selection. Most approaches come from the field of Component-Based Software Development (CBSD), tackle the problem of Commercial-off-the-shelf component selection and use goal-oriented requirements modelling and multi-criteria decision making techniques. Evaluation of the suitability of components is carried out largely manually and partly relies on subjective judgement. However, in dynamic, distributed environments with high demands for transparent selection processes leading to trustworthy, auditable decisions, subjective judgements and vendor claims are not considered sufficient. Furthermore, continuous monitoring and re-evaluation of components after integration is sometimes needed.Objective: This paper describes how an evidence-based approach to component evaluation can improve repeatability and reproducibility of component selection under the following conditions: (1) Functional homogeneity of candidate components and (2) High number of components and selection problem instances.Method: Our evaluation and selection method and tool empirically evaluate candidate components in controlled experiments by applying automated measurements. By analysing the differences to system-development-oriented scenarios, the paper shows how the process of utility analysis can be tailored to fit the problem space, and describes a method geared towards automated evaluation in an empirical setting. We describe tool support and a framework for automated measurements.We further present a taxonomy of decision criteria for the described scenario and discuss the data collection means needed for each category of criteria.Results: To evaluate our approach, we discuss a series of case studies in the area of digital preservation. We analyse the criteria defined in these case studies, classify them according to the taxonomy, and discuss the quantitative coverage of automated measurements.Conclusion: The results of the analysis show that an automated measurement, evaluation and selection framework is necessary and feasible to ensure trusted and repeatable decisions.     

A protocol for multi-agent negotiation in a group-choice decision making process

Negotiation is one of the most important features of agent interactions found in multi-agent systems, because it provides the basis for managing the expectations of the individual negotiating agents, and it enables selecting solutions that satisfy all the agents as much as possible. In order for negotiation to take place between two or more agents there is need for a negotiation protocol that defines the rules of the game; consequently, a variety of agent negotiation protocols have been proposed in literature. However, most of them are inappropriate for Group-Choice Decision Making (GCDM) because they do not explicitly exploit tradeoff to achieve social optimality, and their main focus is solving two-agent negotiation problems such as buyer–seller negotiation. In this paper we present an agent negotiation protocol that facilitates the solving of GCDM problems. The protocol is based on a hybrid of analytic and artificial intelligence techniques. The analytic component of the protocol utilizes a Game Theory model of an n-person general-sum game with complete information to determine the agreement options, while the knowledge-based (artificial intelligence) component of the protocol is similar to the strategic negotiation protocol. Moreover, this paper presents a tradeoff algorithm based on Qualitative Reasoning, which the agents employ to determine the ‘amount’ of tradeoff associated with various agreement options. Finally, the paper presents simulation results that illustrate the operational effectiveness of our agent negotiation protocol.     

Modeling safety considerations and space limitations in piling operations using agent based simulation

Site space availability is an aspect resource that must be taken into consideration when estimating the duration of construction operations. Site space availability has significant effect in meeting target schedule. This paper proposes a model for estimating the productivity of bored piles, taking into consideration safety requirements and space availability. Executing bored piles requires special equipment resources that require space to work and to move. The cost of equipment that is used in bored pile operations ranges from 25% to 30% of the total cost. Therefore, it is deemed important to decide on the exact number of required equipment in piling operations. The proposed model utilizes agent based simulation (ABS) to estimate bored pile productivity depending on the available resources (rig and crane equipment). The model captures the probabilities of equipment breakdowns based on equipment historical data. It also animates movements of equipment taking into consideration safety requirements. A case study is presented to demonstrate the practical features of the proposed ABS model.