Modeling Ambiguity in a Multi-Agent System

This paper investigates the formal pragmatics of ambiguous expressions by modeling ambiguity in a multi-agent system. Such a framework allows us to give a more refined notion of the kind of information that is conveyed by ambiguous expressions. We analyze how ambiguity affects the knowledge of the dialog participants and, especially, what they know about each other after an ambiguous sentence has been uttered. The agents communicate with each other by means of a TELL-function, whose application is constrained by an implementation of some of Grice's maxims. The information states of the multi-agent system itself are represented as a Kripke structures and TELL is an update function on those structures. This framework enables us to distinguish between the information conveyed by ambiguous sentences vs. the information conveyed by disjunctions, and between semantic ambiguity vs. perceived ambiguity.Comment: 7 page

A Multi-Contextual Approach to Modeling the Impact of Critical Highway Work Zones in Large Urban Corridors

Accurate Construction Work Zone (CWZ) impact assessments of unprecedented travel inconvenience to the general public are required for all federally-funded highway infrastructure improvement projects. These assessments are critical, but they are also very difficult to perform. Most existing prediction approaches are project-specific, shortterm, and univariate, thus incapable of benchmarking the potential traffic impact of CWZs for highway construction projects. This study fills these gaps by creating a big-data-based decision-support framework and testing if it can reliably predict the potential impact of a CWZ under arbitrary lane closure scenarios. This study proposes a big-data-based decision-support analytical framework, “Multi-contextual learning for the Impact of Critical Urban highway work Zones” (MICUZ). MICUZ is unique as it models the impact of CWZ operations through a multi-contextual quantitative method utilizing sensored big transportation data. MICUZ was developed through a three-phase modeling process. First, robustness of the collected sensored data was examined through a Wheeler’s repeatability and reproducibility analysis, for the purpose of verifying the homogeneity of the variability of traffic flow data. The analysis results led to a notable conclusion that the proposed framework is feasible due to the relative simplicity and periodicity of highway traffic profiles. Second, a machine-learning algorithm using a Feedforward Neural Networks (FNN) technique was applied to model the multi-contextual aspects of iii long-term traffic flow predictions. The validation study showed that the proposed multi-contextual FNN yields an accurate prediction rate of traffic flow rates and truck percentages. Third, employing these predicted traffic parameters, a curve-fitting modeling technique was implemented to quantify the impact of what-if lane closures on the overall traffic flow. The robustness of the proposed curve-fitting models was then scientifically verified and validated by measuring forecast accuracy. The results of this study convey the fact that MICUZ would recognize how stereotypical regional traffic patterns react to existing CWZs and lane closure tactics, and quantify the probable but reliable travel time delays at CWZs in heavily trafficked urban cores. The proposed framework provides a rigorous theoretical basis for comparatively analyzing what-if construction scenarios, enabling engineers and planners to choose the most efficient transportation management plans much more quickly and accurately

Business Process Model Reuse In A Multi-Channel / Multi-Product Environment–Problem Identification And Tentative Design

Business Process Modeling has become a common activity in organisations. However, as the number of process models increases, so too does the number of duplicated models increase and the level of process model reuse has been found to be surprisingly low. In organisations which operate in an environment with multiple channels, products and customer types, complete process model reuse becomes especially challenging. Without a well-defined approach, such an environment could easily result in dozens of slight variations of what is essentially the same process which will lead to future model and repository management challenges. In response to this problem this paper reviews the literature of complete business process reuse in a multi-channel / multi-product environment. We find that there is a clear gap in the literature in terms of practical solutions that address the problem described but were able to distil five practices that can increase complete model reuse. This review and the practices described will help practitioners grappling with these challenges and paves the way for further needed research on this problem

A study on systems modeling frameworks and their interoperability

'What is in a model?' is a question that any systems modeler has asked at least once when working with a biological system. To answer this question we must understand how every species and reaction in a model are biologically related to each other, which in the case of cellular systems requires an understanding of local protein and gene interactions at the domain level. Typical modeling approaches like reaction-networks do not explicitly encode this information, which can obfuscate the assumptions made by a model and limit their further analysis, comparison and re-use. Additionally, the multi-state nature of the elements in a model can lead to a combinatorial explosion of the interactions in the model, pushing reaction-networks to their limits. Rule-based modeling is a modeling paradigm that addresses these issues through the use of a graph-based representation of each species's interaction domains and their local interactions. In this paradigm, reaction \emph{rules} describe a class of reactions and are represented as graph rewriting operations that encode biological processes like bond formation and post-translational modification. This allows for a concise, explicit encoding of how species interact, modify and are related to each other while at the same time managing the enormous number of potential molecular interactions in a multi-state system. In this work I present two different approaches that aim to bring the benefits of rule-based modeling's graph representation to other modeling paradigms: \emph{Atomizer} and \emph{MCell-R}. Atomizer is an algorithm for the extraction of structural and process information from reaction-network models, which I use to encode the model in a rule-based format. Once we have an explicit understanding of what is in a model and how entities in a model interact, we can then perform meta-modeling operations like model analysis, alignment, comparison and visualization, which I demonstrate through the application of Atomizer to a large dataset of reaction network models. MCell-R is a framework for the efficient modeling and simulation of multi-state, multi-component spatial systems. The framework consists of an integration of the NFsim rule-based simulation engine together with the MCell spatial modeling system, which highlights the utility of bringing rule-based paradigms into reaction based platforms

Multi-agent systems for power engineering applications - part 1 : Concepts, approaches and technical challenges

This is the first part of a 2-part paper that has arisen from the work of the IEEE Power Engineering Society's Multi-Agent Systems (MAS) Working Group. Part 1 of the paper examines the potential value of MAS technology to the power industry. In terms of contribution, it describes fundamental concepts and approaches within the field of multi-agent systems that are appropriate to power engineering applications. As well as presenting a comprehensive review of the meaningful power engineering applications for which MAS are being investigated, it also defines the technical issues which must be addressed in order to accelerate and facilitate the uptake of the technology within the power and energy sector. Part 2 of the paper explores the decisions inherent in engineering multi-agent systems for applications in the power and energy sector and offers guidance and recommendations on how MAS can be designed and implemented