DCOP FOR SMART HOMES: A CASE STUDY

The aim of this article is to bring forth the issue of integrating the services provided by intelligent artifacts in Ambient Intelligence applications. Specifically, we propose a Distributed Constraint Optimization procedure for achieving a functional integration of intelligent artifacts in a smart home. To this end, we employ Adopt-N , a state-of-the-art algorithm for solving Distributed Constraint Optimization Problems (DCOP). This article attempts to state the smart home coordination problem in general terms, and provides the details of a DCOP-based approach by describing a case study taken from the RoboCare project. More specifically, we show how (1) DCOP is a convenient metaphor for casting smart home coordination problems, and (2) the specific features which distinguish Adopt-N from other algorithms for DCOP represent a strong asset in the smart home domain.     

Solving job shop scheduling with setup times through constraint-based iterative sampling: an experimental analysis

This paper presents a heuristic algorithm for solving a job-shop scheduling problem with sequence dependent setup times and min/max separation constraints among the activities (SDST-JSSP/max). The algorithm relies on a core constraint-based search procedure, which generates consistent orderings of activities that require the same resource by incrementally imposing precedence constraints on a temporally feasible solution. Key to the effectiveness of the search procedure is a conflict sampling method biased toward selection of most critical conflicts and coupled with a non-deterministic choice heuristic to guide the base conflict resolution process. This constraint-based search is then embedded within a larger iterative-sampling search framework to broaden search space coverage and promote solution optimization. The efficacy of the overall heuristic algorithm is demonstrated empirically both on a set of previously studied job-shop scheduling benchmark problems with sequence dependent setup times and by introducing a new benchmark with setups and generalized precedence constraints.     

Distributed artificial intelligence from a socio-cognitive standpoint: Looking at reasons for interaction

Distributed Artificial Intelligence (DAI) deals with computational systems where several intelligent components interact in a common environment. This paper is aimed at pointing out and fostering the exchange between DAI and cognitive and social science in order to deal with the issues of interaction, and in particular with the reasons and possible strategies for social behaviour in multi-agent interaction is also described which is motivated by requirements of cognitive plausibility and grounded the notions of power, dependence and help. Connections with human-computer interaction are also suggested.     

Microalgal phycocyanin productivity: strategies for phyco‐valorization

Phyco‐valorization is the exploitation of microalgae and microalgal chemicals as valuable products. This paper discusses the optimization of microalgal bioreactor‐based systems for C‐phycocyanin pigment production. Various aspects contributing to system development and enhancement of phycocyanin productivity are described. A wide range of potential microalgal species have been identified for phycocyanin production; the selection of a species for mass culturing can be determined by desired bioreactor trophic mode and symbiotic relations. Research has demonstrated that species amenability to local lighting and climatic conditions, and to variations in bioreactor substrate concentrations and operational parameters, have significant impact on phycocyanin production. The simultaneous optimization of all factors contributing to system productivity may be accomplished efficiently through process modelling. A summary of established models for microalgal phycocyanin production is presented. A suggested strategy for increasing economic viability of phycocyanin production systems is their application in integrated resource recovery. Through the incorporation of phycocyanin productivity optimization principles within a phycoremediation process, the valorization of waste resources may be achieved. The simultaneous economic potential and environmentally‐forward concept of phyco‐valorization through phycocyanin production is a promising application of microalgal biotechnology awaiting further development for industrial implementation. © 2015 Society of Chemical Industry     

User-oriented problem abstractions in scheduling

In this paper we describe a modeling framework aimed at facilitating the customization and deployment of artificial intelligence (AI) scheduling technology in real-world contexts. Specifically, we describe an architecture aimed at facilitating software product line development in the context of scheduling systems. The framework is based on two layers of abstraction: a first layer providing an interface with the scheduling technology, on top of which we define a formalism to abstract domain-specific concepts. We show how this two-layer modeling framework provides a versatile formalism for defining user-oriented problem abstractions, which is pivotal for facilitating interaction between domain experts and technologists. Moreover, we describe a graphical user interface (GUI)-enhanced tool which allows the domain expert to interact with the underlying core scheduling technology in domain-specific terms. This is achieved by automatically instantiating an abstract GUI template on top of the second modeling layer.     

Iterative flattening search for resource constrained scheduling

Iterative flattening search (ifs) is a meta-heuristic strategy for solving multi-capacity scheduling problems. Given an initial solution, ifs iteratively applies: (1) a relaxation-step, in which a subset of scheduling decisions are randomly retracted from the current solution; and (2) a flattening-step, in which a new solution is incrementally recomputed from this partial schedule. Whenever a better solution is found, it is retained, and, upon termination, the best solution found during the search is returned. Prior research has shown ifs to be an effective and scalable heuristic procedure for minimizing schedule makespan in multi-capacity resource settings. In this paper we experimentally investigate the impact on ifs performance of algorithmic variants of the flattening step. The variants considered are distinguished by different computational requirements and correspondingly vary in the type and depth of search performed. The analysis is centered around the idea that given a time bound to the overall optimization procedure, the ifs optimization process is driven by two different and contrasting mechanisms: the random sampling performed by iteratively applying the “relaxation/flattening” cycle and the search conducted within the constituent flattening procedure. On one hand, one might expect that efficiency of the flattening process is key: the faster the flattening procedure, the greater the number of iterations (and number of sampled solutions) for a given time bound; and hence the greater the probability of finding better quality solutions. On the other hand, the use of more accurate (and more costly) flattening procedures can increase the probability of obtaining better quality solutions even if their greater computational cost reduces the number of ifs iterations. Comparative results on well-studied benchmark problems are presented that clarify this tradeoff with respect to previously proposed flattening strategies, identify qualitative guidelines for the design of effective ifs procedures, and suggest some new directions for future work in this area.     

Mixed-Initiative Issues in an Agent-Based Meeting Scheduler

This paper concerns mixed-initiative interaction between users and agents. After classifying agents according to their task and their interactivity with the user, the critical aspects of delegation-based interaction are outlined. Then MASMA, an agent system for distributed meeting scheduling, is described, and the solutions developed to control interaction are explained in detail. The issues addressed concern: the agent capability of adapting its behavior to the user it is supporting; the solution adopted to control the shift of initiative between personal agents, their users and other agents in the environment; the availability of features, e.g. the inspection mechanism, that endow the user with a further level of control to enhance his sense of trust in the agent.     

Single-Walled Carbon Nanotube (SWCNT)-induced interstitial fibrosis in the lungs of rats is associated with increased levels of PDGF mRNA and the formation of unique intercellular carbon structures that bridge alveolar macrophages In Situ

Background  

Nanotechnology is a rapidly advancing industry with many new products already available to the public. Therefore, it is essential to gain an understanding of the possible health risks associated with exposure to nanomaterials and to identify biomarkers of exposure. In this study, we investigated the fibrogenic potential of SWCNT synthesized by chemical vapor deposition using cobalt (Co) and molybdenum (Mo) as catalysts. Following a single oropharyngeal aspiration of SWCNT in rats, we evaluated lung histopathology, cell proliferation, and growth factor mRNAs at 1 and 21 days post-exposure. Comparisons were made to vehicle alone (saline containing a biocompatible nonionic surfactant), inert carbon black (CB) nanoparticles, or vanadium pentoxide (V₂O₅) as a known inducer of fibrosis.     

Knowledge-based adaptive agents for manufacturing domains

Modern production systems are increasingly using artificial agents (e.g., robots) of different kinds. Ideally, these agents should be able to recognize the state of the world, to act optimizing their work toward the achievement of a set of goals, to change the plan of action when problems arise, and to collaborate with other artificial and human agents. The development of such an ideal agent presents several challenges. We concentrate on two of them: the construction of a single and coherent knowledge base which includes different types of knowledge with which to understand and reason on the state of the world in a human-like way; and the isolation of types of contexts that the agent can exploit to make sense of the actual situation from a perspective and to interact accordingly with humans. We show how to build such a knowledge base (KB) and how it can be updated as time passes. The KB we propose is based on a foundational ontology, is cognitively inspired, and includes a notion of context to discriminate information. The KB has been partially implemented to test the use and suitability of the knowledge representation for the agent’s control model via a temporal planning and execution system. Some experimental results showing the feasibility of our approach are reported.

     

Solve-and-robustify

Goal separation is often a fruitful approach when solving complex problems. It provides a way to focus on relevant aspects in a stepwise fashion and hence bound the problem solving scope along a specific direction at any point. This work applies goal separation to the problem of synthesizing robust schedules. The problem is addressed by separating the phase of problem solution, which may pursue a standard optimization criterion (e.g., minimal makespan), from a subsequent phase of solution robustification in which a more flexible set of solutions is obtained and compactly represented through a temporal graph, called a Partial Order Schedule ( ). The key advantage of a is that it provides the capability to promptly respond to temporal changes (e.g., activity duration changes or activity start-time delays) and to hedge against further changes (e.g., new activities to perform or unexpected variations in resource capacity). On the one hand, the paper focuses on specific heuristic algorithms for synthesis of s, starting from a pre-existing schedule (hence the name Solve-and-Robustify). Different extensions of a technique called chaining, which progressively introduces temporal flexibility into the representation of the solution, are introduced and evaluated. These extensions follow from the fact that in multi-capacitated resource settings more than one can be derived from a specific fixed-times solution via chaining, and carry out a search for the most robust alternative. On the other hand, an additional analysis is performed to investigate the performance gain possible by further broadening the search process to consider multiple initial seed solutions. A detailed experimental analysis using state-of-the-art rcpsp/max  benchmarks is carried out to demonstrate the performance advantage of these more sophisticated solve and robustify procedures, corroborating prior results obtained on smaller problems and also indicating how this leverage increases as problem size is increased.