Advancing artificial intelligence research and dissemination through conference series: Benchmark,scientific impact and the MICAI experience

This article presents an overview, analysis and benchmark of the best-known artificial intelligence (AI) conferences, including the Mexican International Conference on Artificial Intelligence (MICAI) conference series, and describes how MICAI has contributed to both the growth of artificial intelligence (AI) research in Mexico and the advancement of AI research worldwide. Among the prestigious AI conferences examined are the IJCAI, AAAI, ECAI, IBERAMIA, AAJCAI and PRICAI. Features analyzed include number of papers, acceptance rate and the h index as a measure of the scientific impact. The MICAI has been held in Mexico since 2000, when the National Meeting on AI, held by the Mexican Society for Artificial Intelligence (SMIA) since 1983, and the International Symposium on Artificial Intelligence (ISAI), organized by Tecnológico de Monterrey (ITESM) since 1988, merged into a single conference. Conference trends and future developments are also explained.     

On algorithmic natural language analysis and understanding

In this article an attempt is made to substantiate that understanding and analyzing natural language texts is not algorithmic, that is to show that there is a natural boundary separating the human from the algorithm supposed to model understanding and syntax analysis. Our main concern will be to discriminate the human from the algorithm with respect to the notion of truth; the analogous discrimination with respect to understanding and analyzing natural language will then be shown reduceable to the first mentioned one.To this end we cannot apply directly the classic results, such for example as Goedel's incompleteness theorem. In fact all these results attempt to show inevitable shortcomings in the formalism's way of handling some important tasks. Now, however, we are confronted with the problem of differences between the human and the formalism. This problem differs from the one set in Hilbert's programme in that now a formalism is wanted which would act, (e.g. in handling the notion of truth) as imperfectly as the human does. We attempt to show such a formalism to be impossible.Our analysis cannot be a mathematical one (and not even a formal deduction in general) as for example it is impossible to obtain formally the conclusion “intelligence is not formalizable” after having started the investigation with formalizing intelligence. This certainly calls down methodological difficulties, which better are avoided, and apparently weakens the discourse. But, those not having what they like have to like what they have. Besides we are not within a deductive science and there is no reason to limit our methods to deductive ones. And, after all, we are in good company: the present investigation seems methodologically related to metaphysical discourses of physics (such, e.g. as Gendanken-experiments, first named but not first used by Einstein) or speculations of astro-physics. Finally, the problem of limitations for Artificial Intelligence, as well as for Computer Science in general, just exists independently from whether you are able to handle it mathematically or not.     

Further towards a taxonomy of agent-based simulation models in environmental management

Agent-based simulation (ABS) is being increasingly used in environmental management. However, the efficient and effective use of ABS for environmental modelling is hindered by the fact that there is no fixed and clear definition of what an ABS is or even what an agent should be. Terminology has proliferated and definitions of agency have been drawn from an application area (Distributed Artificial Intelligence) which is not wholly relevant to the task of environmental simulation. This situation leaves modellers with little practical support for clearly identifying ABS techniques and how to implement them.This paper is intended to provide an overview of agent-based simulation in environmental modelling so that modellers can link their requirements to the current state of the art in the techniques that are currently used to satisfy them. Terminology is clarified and then simplified to two key existing terms, agent-based modelling and multi-agent simulation, which represent subtly different approaches to ABS, reflected in their respective artificial life (A-life) and distributed artificial intelligence roots. A representative set of case studies are reviewed, from which a classification scheme is developed as a stepping-stone to developing a taxonomy. The taxonomy can then be used by modellers to match ABS techniques to their requirements.     

Robustness,stability, recoverability,and reliability in constraint satisfaction problems

Many real-world problems in Artificial Intelligence (AI) as well as in other areas of computer science and engineering can be efficiently modeled and solved using constraint programming techniques. In many real-world scenarios the problem is partially known, imprecise and dynamic such that some effects of actions are undesired and/or several unforeseen incidences or changes can occur. Whereas expressivity, efficiency, and optimality have been the typical goals in the area, there are several issues regarding robustness that have a clear relevance in dynamic Constraint Satisfaction Problems (CSPs). However, there is still no clear and common definition of robustness-related concepts in CSPs. In this paper, we propose two clearly differentiated definitions for robustness and stability in CSP solutions. We also introduce the concepts of recoverability and reliability, which arise in temporal CSPs. All these definitions are based on related well-known concepts, which are addressed in engineering and other related areas.     

Multi-source information fusion for smart health with artificial intelligence

The rapid developments in Artificial Intelligence present an opportunity for the research community to provide and advance Smart Health for the well-being of our society. By considering the availability of multi-source information and heterogeneous data in the era of Big Data, this Special Issue explores the theories, methodologies and possible breakthroughs that have designed and adopted information fusion for Smart Health powered by recent Artificial Intelligence advances. Specifically, this Special Issue focuses on three questions; How to achieve and realize human-level intelligence in Smart Health, How to achieve and benefit Smart Health from a multi-disciplinary balance, and How to utilize the power of Big Data for Smart Health. The Special Issue is a great success, with a small number of quality studies carefully selected from an overwhelming amount of contributions.     

Artificial intelligence and robotics

Since Robotics is the field concerned with the connection of perception to action, Artificial Intelligence must have a central role in Robotics if the connection is to be intelligent. Artificial Intelligence addresses the crucial questions of: what knowledge is required in any aspect of thinking; how should that knowledge be represented; and how should that knowledge be used. Robotics challenges AI by forcing it to deal with real objects in the real world. Techniques and representations developed for purely cognitive problems, often in toy domains, do not necessarily extend to meet the challenge. Robots combine mechanical effectors, sensors, and computers. AI has made significant contributions to each component. We review AI contributions to perception and reasoning about physical objects. Such reasoning concerns space, path-planning, uncertainty, and compliance. We conclude with three examples that illustrate the kinds of reasoning or problem-solving abilities we would like to endow robots with and that we believe are worthy goals of both Robotics and Artificial Intelligence, being within reach of both.     

Computing ideal sceptical argumentation

We present two dialectic procedures for the sceptical ideal semantics for argumentation. The first procedure is defined in terms of dispute trees, for abstract argumentation frameworks. The second procedure is defined in dialectical terms, for assumption-based argumentation frameworks. The procedures are adapted from (variants of) corresponding procedures for computing the credulous admissible semantics for assumption-based argumentation, proposed in [P.M. Dung, R.A. Kowalski, F. Toni, Dialectic proof procedures for assumption-based, admissible argumentation, Artificial Intelligence 170 (2006) 114–159]. We prove that the first procedure is sound and complete, and the second procedure is sound in general and complete for a special but natural class of assumption-based argumentation frameworks, that we refer to as p-acyclic. We also prove that in the case of p-acyclic assumption-based argumentation frameworks (a variant of) the procedure of [P.M. Dung, R.A. Kowalski, F. Toni, Dialectic proof procedures for assumption-based, admissible argumentation, Artificial Intelligence 170 (2006) 114–159] for the admissible semantics is complete. Finally, we present a variant of the procedure of [P.M. Dung, R.A. Kowalski, F. Toni, Dialectic proof procedures for assumption-based, admissible argumentation, Artificial Intelligence 170 (2006) 114–159] that is sound for the sceptical grounded semantics.     

Introduction to the special issue

Artificial intelligence is a productive research paradigm for a variety of problems that arise in the world of investments This article provides an introduction to this special issue of Applied Artificial Intelligence - Artificial Intelligence Applications on Wall Street - which is devoted to financial AI applications The articles in this volume are derived from papers presented at the Third International Conference on Artificial Intelligence Applications on Wall Street held in June 1995 in New York City     

Achieving a sustainable interoperability of standards

Agile enterprises and networks are required to continuously evolve so as to adequately respond to a dynamic, global and very competitive business environment. Unfortunately, this continuous change requirement affects the capacity of the enterprise to effectively interoperate internally and externally. Adding to this problem, the standards typically used as pillars for enterprise and network management and (inter)operation are themselves subject to continuous change and often bring their own interoperability, inconsistency and overlap problems. It is essential that such concerns are identified and addressed in a sustainable manner, i.e. taking into account the life cycles of all entities involved as their interoperability capabilities and requirements change along their lives. This paper focuses on interoperability issues specific to standards and proposes a novel and original way forward based on principles tested in previous case studies combining elements from the Enterprise Interoperability, Architecture and Integration, Collaborative Networks and Artificial Intelligence bodies of knowledge.     

Finding optimum route of electrical energy transmission line using multi-criteria with Q-learning

Due to an increasing energy requirement the consideration of route determination is becoming important. The aim of this project is to find an optimum result considering its important criteria. Finding an optimum route is a complex problem. It does not mean the shortest path to the problem. It is important to find the best way under the criterion that is determined by experts. Because of this we did not use the classical shortest path algorithm and we applied one of algorithms of the Artificial Intelligence. In this work, Geographic Information System (GIS)-based energy transmission route planning had been performed. In this optimization, using Multiagent Systems (MAS) which is a subdirectory of Distributed Artificial Intelligence the multi-criteria affecting energy transmission line (ETL) had been severally analyzed. The application had been actualized on the Selcuk University Campus Area. Therefore, the digital map of the campus area particularly had been composed containing of relevant criteria. Using Q- learning Algorithm of Multiagent System the optimum route had been determined.     

What evolutionary game theory tells us about multiagent learning

This paper discusses If multi-agent learning is the answer, what is the question? [Y. Shoham, R. Powers, T. Grenager, If multi-agent learning is the answer, what is the question? Artificial Intelligence 171 (7) (2007) 365-377, this issue] from the perspective of evolutionary game theory. We briefly discuss the concepts of evolutionary game theory, and examine the main conclusions from [Y. Shoham, R. Powers, T. Grenager, If multi-agent learning is the answer, what is the question? Artificial Intelligence 171 (7) (2007) 365-377, this issue] with respect to some of our previous work. Overall we find much to agree with, concluding, however, that the central concerns of multiagent learning are rather narrow compared with the broad variety of work identified in [Y. Shoham, R. Powers, T. Grenager, If multi-agent learning is the answer, what is the question? Artificial Inteligence 171 (7) (2007) 365-377, this issue].     

Ensuring reliability in B2B services: Fault tolerant inter-organizational workflows

In the age of Business-to-Business (B2B) collaboration, ensuring reliability of workflows underlying inter-organizational business processes is of significant importance. There are, however, quite a few challenges towards achieving seamless operation. Such challenges arise from heterogeneity in infrastructure and coordination mechanism at participant organizations, as well as time and cost associated with recovery from failure. Our research presents foundations for a reliable scheme for recovery from failure of workflow processes spanning through multiple business entities. First, a system model is adapted from the mobile computing literature that serves to establish the requirements to be enforced by each participating organization. In our model, we adopt the Maximal Sequence Path (MSP) approach from Yoo et al. (Lecture Notes in Artificial Intelligence 2132:222–236, 2001), as a means of decomposing workflows into mobile agent-driven processes that communicate via web services at each organization. This decomposition ensures defining logical points within the dynamics of a workflow instance for locating accurate and consistent states of the system for recovery in case of a failure. Then, a set of algorithms for various business scenarios are developed and presented as practical solutions. These algorithms are shown to create checkpoints such that the system is always in a globally consistent state. As such, these algorithms constitute a set of standards that can be incorporated in business process management suites that support reliable inter-organizational collaboration.     

Reasoning about river basins: WaWO+ revisited

This paper characterizes part of an interdisciplinary research effort on Artificial Intelligence (AI) techniques and tools applied to Environmental Decision-Support Systems (EDSS). WaWO+ the ontology we present here, provides a set of concepts that are queried, advertised and used to support reasoning about and the management of urban water resources in complex scenarios as a River Basin. The goal of this research is to increase efficiency in Data and Knowledge interoperability and data integration among heterogeneous environmental data sources (e.g., software agents) using an explicit, machine understandable ontology to facilitate urban water resources management within a River Basin.     

Prototypes construction from partial rankings to characterize the attractiveness of companies in Belgium

What are the most relevant factors to be considered by employees when searching for an employer? The answer to this question poses valuable knowledge from the Business Intelligence viewpoint since it allows companies to retain personnel and attract competent employees. It leads to an increase in sales of their products or services, therefore remaining competitive across similar companies in the market. In this paper we assess the attractiveness of companies in Belgium by using a new two-stage methodology based on Artificial Intelligence techniques. The proposed method allows constructing high-quality prototypes from partial rankings indicating experts’ preferences. Being more explicit, in the first step we propose a fuzzy clustering algorithm for partial rankings called fuzzy c-aggregation. This algorithm is based on the well-known fuzzy c-means procedure and uses the Hausdorff distance as dissimilarity functional and a counting strategy for updating the center of each cluster. However, we cannot ensure the optimality of such prototypes, and therefore more accurate prototypes must be derived. That is why the second step is focused on solving the extended Kemeny ranking problem for each discovered cluster taking into account the estimated membership matrix. To accomplish that, we adopt an optimization method based on Swarm Intelligence that exploits a colony of artificial ants. Several simulations show the effectiveness of the proposal for the real-world problem under investigation.     

Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning

We model reinforcement learning as the problem of learning to control a partially observable Markov decision process (POMDP) and focus on gradient ascent approaches to this problem. In an earlier work (2001, J. Artificial Intelligence Res.14) we introduced GPOMDP, an algorithm for estimating the performance gradient of a POMDP from a single sample path, and we proved that this algorithm almost surely converges to an approximation to the gradient. In this paper, we provide a convergence rate for the estimates produced by GPOMDP and give an improved bound on the approximation error of these estimates. Both of these bounds are in terms of mixing times of the POMDP.     

Toward a comprehensive treatment of temporal constraints about periodic events

In this article, we propose an Allen‐like approach to deal with different types of temporal constraints about periodic events. We consider the different components of such constraints (thus, unlike Allen, we also take into account quantitative constraints) including frame times, user‐defined periods, qualitative temporal constraints, and numeric quantifiers and the interactions between such components. We propose a specialized high‐level formalism to represent temporal constraints about periodic events; temporal reasoning on the formalism is performed by a path‐consistency algorithm repeatedly applying our operations of inversion, intersection, and composition and by a specialized reasoner about periods and numeric quantification. The high‐level formalism has been designed in such a way that different types of temporal constraints about periodic events can be represented in a compact and (hopefully) user‐friendly way and path‐consistency‐based temporal reasoning on the formalism can be performed in polynomial time. We also prove that our definitions of inversion, intersection, and composition and, thus, of our path‐consistency algorithm, are correct. This article also sketches the general architecture of the temporal manager for periodic events (TeMP⁺), that has been designed on the basis of our approach. As a working example, we show an application of our approach to scheduling in a school. © 2003 Wiley Periodicals, Inc.     

Pre-run-time scheduling in real-time systems: Current researches and Artificial Intelligence perspectives

This paper presents the taxonomy of real-time systems with special emphasize on pre-run-time scheduling problem. Firstly, we present real-time systems, real-time tasks, timing, precedence and exclusion constraints. Then, we describe the problem of pre-run-time scheduling of tasks under constraints. After that, we present the most existing efficient techniques to deal with the latter problem. We summarize the discussion of existing techniques and possible research perspectives after surveying the Artificial Intelligence’s point of view about the problem of pre-run-time scheduling of real-time tasks. The Artificial Intelligence survey includes Constraint Satisfaction Problems class since pre-run-time scheduling belongs to the latter class. The Artificial Intelligence survey includes also Path-finding Problems from which intelligent algorithms could be observed such as Learning-Real-Time-A1(LRTA1) thanks to its important properties (optimality, linear space complexity and determinism). The development of an algorithm like LRTA1 to solve Constraints Satisfaction Problems and particularly the pre-run-time scheduling of real-time tasks problem is one clear research direction to deal with large-scale real-time systems. The overall objective of this paper is to show what are the perspectives to Artificial Intelligence literature that could be beneficial firstly to Artificial Intelligence community itself and secondly to real-time systems community.     

RETRACTED ARTICLE: A multi-agent architectural solution for coherent distributed reconfigurations of function blocks

The paper deals with distributed Multi-Agent Reconfigurable Embedded Control Systems following the International Industrial Standard IEC61499 in which a Function Block (abbreviated by FB) is an event-triggered software component owning data and a control application is a network of distributed blocks that should satisfy functional and temporal properties according to user requirements. We define an architecture of reconfigurable multi-agent systems in which a Reconfiguration Agent is affected to each device of the execution environment to apply local reconfigurations, and a Coordination Agent is proposed for coordinations between devices in order to guarantee safe and adequate distributed reconfigurations. A Communication Protocol is proposed to handle coordinations between agents by using well-defined Coordination Matrices. We specify both reconfiguration agents to be modelled by nested state machines, and the Coordination Agent according to the formalism Net Condition-Event Systems (Abbreviated by NCES) which is an extension of Petri nets. To validate the whole architecture, we check by applying the model checker SESA in each device functional and temporal properties to be described according to the temporal logic “Computation Tree Logic”. We have also to check all possible coordinations between devices by verifying that whenever a reconfiguration is applied in a device, the Coordination Agent and other concerned devices react as described in user requirements. We present a tool applying simulations of this distributed architecture in order to check interactions and reactivities of agents. The paper’s contributions are applied to two Benchmark Production Systems available in our research laboratory.