General Framework For Representation Research Articles

A general multi-agent epistemic planner based on higher-order belief change

In recent years, multi-agent epistemic planning has received attention from both dynamic logic and planning communities. Existing implementations of multi-agent epistemic planning are based on compilation into classical planning and suffer from various limitations, such as generating only linear plans, restriction to public actions, and incapability to handle disjunctive beliefs. In this paper, we consider centralized multi-agent epistemic planning from the viewpoint of a third person who coordinates all the agents to achieve the goal. We treat contingent planning, resulting in nonlinear plans. We model private actions and hence handle beliefs, formalized with the multi-agent KD45 logic. We handle static propositional common knowledge, which we call constraints. For such planning settings, we propose a general representation framework where the initial knowledge base (KB) and the goal, the preconditions and effects of actions can be arbitrary KD45n formulas, and the solution is an action tree branching on sensing results. In this framework, the progression of KBs w.r.t. actions is achieved through the operation of belief revision or update on KD45n formulas, that is, higher-order belief revision or update. To support efficient reasoning and progression, we make use of a normal form for KD45n called alternating cover disjunctive formulas (ACDFs). We propose reasoning, revision and update algorithms for ACDFs. Based on these algorithms, adapting the PrAO algorithm for contingent planning from the literature, we implemented a multi-agent epistemic planner called MEPK. Our experimental results show the viability of our approach.

A lossless compression method for multi-component medical images based on big data mining

In disease diagnosis, medical image plays an important part. Its lossless compression is pretty critical, which directly determines the requirement of local storage space and communication bandwidth of remote medical systems, so as to help the diagnosis and treatment of patients. There are two extraordinary properties related to medical images: lossless and similarity. How to take advantage of these two properties to reduce the information needed to represent an image is the key point of compression. In this paper, we employ the big data mining to set up the image codebook. That is, to find the basic components of images. We propose a soft compression algorithm for multi-component medical images, which can exactly reflect the fundamental structure of images. A general representation framework for image compression is also put forward and the results indicate that our developed soft compression algorithm can outperform the popular benchmarks PNG and JPEG2000 in terms of compression ratio.

Emotional biologically inspired cognitive architecture

Abstract Human-like artificial emotional intelligence is vital for integration of future robots into the human society. This work introduces a general framework for representation and processing of emotional contents in a cognitive architecture, called “emotional biologically inspired cognitive architecture” (eBICA). Unlike in previous attempts, in this framework emotional elements are added virtually to all cognitive representations and processes by modifying the main building blocks of the prototype architectures. The key elements are appraisals associated as attributes with schemas and mental states, moral schemas that control patterns of appraisals and represent social emotions, and semantic spaces that give values to appraisals. Proposed principles are tested in an experiment involving human subjects and virtual agents, based on a simple paradigm in imaginary virtual world. It is shown that with moral schemas, but probably not without them, eBICA can account for human behavior in the selected paradigm. The model sheds light on clustering of social emotions and allows for their elegant mathematical description. The new framework will be suitable for implementation of believable emotional intelligence in artifacts, necessary for emotionally informed behavior, collaboration of virtual partners with humans, and self-regulated learning of virtual agents.

Optimization of Bioreactor Using Metabolic Control Analysis Approach

A new methodology based on a metabolic control analysis (MCA) approach is developed for the optimization of continuous cascade bioreactor system. A general framework for representation of a cascade bioreactor system consisting of a large number of reactors as a single network is proposed. The kinetic and transport processes occurring in the system are represented as a reaction network with appropriate stoichiometry. Such representation of the bioreactor systems makes it amenable to the direct application of the MCA approach. The process sensitivity information is extracted using MCA methodology in the form of flux and concentration control coefficients. The process sensitivity information is shown to be a useful guide for determining the choice of decision variables for the purpose of optimization. A generalized problem of optimization of the bioreactor is formulated in which the decision variables are the operating conditions and kinetic parameters. The gradient of the objective function to be maximized with respect to all decision variables is obtained in the form of response coefficients. This gradient information can be used in any gradient-based optimization algorithm. The efficiency of the proposed technique is demonstrated with two examples taken from literature: biotransformation of crotonobetaine and alcohol fermentation in cascade bioreactor system.

A logical foundation for representation of clinical data.

A general framework for representation of clinical data that provides a declarative semantics of terms and that allows developers to define explicitly the relationships among both terms and combinations of terms. Use of conceptual graphs as a standard representation of logic and of an existing standardized vocabulary, the Systematized Nomenclature of Medicine (SNOMED International), for lexical elements. Concepts such as time, anatomy, and uncertainty must be modeled explicitly in a way that allows relation of these foundational concepts to surface-level clinical descriptions in a uniform manner. The proposed framework was used to model a simple radiology report, which included temporal references. Formal logic provides a framework for formalizing the representation of medical concepts. Actual implementations will be required to evaluate the practicality of this approach.

Encapsulation constructs in prolog

Encapsulation is a well-established concept that has been used extensively in programming languages as a tool for disciplined and reliable program organization. In this paper we try to elucidate the critical issues involved, when introducing encapsulation constructs in the programming language PROLOG. It is argued that two different forms of encapsulation are needed in order to incorporate modules and abstract data types into the language. We present a general representation framework which can be used as the basis for the implementaion of various modular systems. We also describe a construct ADT which is used for the encapsulation of user defined abstract data types, and an interpreter that handles the execution of PROLOG goals in the context of modules and abstract data types.