Artificial Intelligence in Hungary - The First 20 Years = Mesterséges intelligencia Magyarországon - az első 20 év

A magyarországi mesterséges intelligencia kutatások történetéről 1996-ban készített áttekintés 2006-ban korszerűsített változata, bőséges irodalom jegyzékkel

Magyar Mesterséges Intelligencia Bibliográfia : Válogatás az 1988-96 között (esetenként korábban) megjelent publikációkból

Tartalom: referált folyóiratokban, konferencia kiadványokban, tanulmánykötetekben megjelent dolgozatok, könyvek, tankönyvek, disszertációk referenciáit, közel 190 magyar szerző/társszerző 400 (tárgyszavazott) dolgozatát tartalmazza. Függelékében az Új ALAPLAP folyóirat Jakab Ágnes által szerkesztett TUDÁSTECHNOLÓGIA c. tematikus MI-sorozat dolgozatainak jegyzéke található. Az anyagok az NJSZT által Budapesten szervezett ECAI’96 konferenciát kísérő kiállításra készültek. A Bibliográfia és a hozzá kapcsolódó Reprint Gyűjtemény az NJSZT standján volt kiállítva, míg az OMIKK adatbázisában való keresést egy oda kihelyezett terminál biztosította. A tárgyszavazást és az adatfelvitelt Kladiva Ottmár (OMIKK) irányította

Planning and Proof Planning

. The paper adresses proof planning as a specific AI planning. It describes some peculiarities of proof planning and discusses some possible cross-fertilization of planning and proof planning. 1 Introduction Planning is an established area of Artificial Intelligence (AI) whereas proof planning introduced by Bundy in [2] still lives in its childhood. This means that the development of proof planning needs maturing impulses and the natural questions arise What can proof planning learn from its Big Brother planning?' and What are the specific characteristics of the proof planning domain that determine the answer?'. In turn for planning, the analysis of approaches points to a need of mature techniques for practical planning. Drummond [8], e.g., analyzed approaches with the conclusion that the success of Nonlin, SIPE, and O-Plan in practical planning can be attributed to hierarchical action expansion, the explicit representation of a plan's causal structure, and a very simple form of propo..

The roles of artificial intelligence and knowledge management in emergency telecommunications

Over the past decade, the international community has recognized the substantive role modern telecommunications can play in disaster relief operations and humanitarian actions.The Tampere Convention on “Emergency Telecommunications” was an initiative to facilitate these activities, endorsed by various international conferences.The role of artificial intelligence and knowledge management in emergency telecommunications could be tremendous, with applications potentially ranging from network and workflow management to training and decision support.In the future, a greater convergence among different technologies, artificial intelligence and knowledge management included, in the service of emergency telecommunications could be foreseen, thus achieving the noble goal of utilizing modern information and communications technologies for disaster mitigation

Distributed constraint optimization with structured resource constraints

Distributed constraint optimization (DCOP) provides a framework for coordinated decision making by a team of agents. Often, during the decision making, capacity constraints on agents ’ resource consumption must be taken into account. To address such scenarios, an extension of DCOP- Resource Constrained DCOP- has been proposed. However, certain type of resources have an additional structure associated with them and exploiting it can result in more efficient algorithms than possible with a general framework. An example of these are distribution networks, where the flow of a commodity from sources to sinks is limited by the flow capacity of edges. We present a new model of structured resource constraints that exploits the acyclicity and the flow conservation property of distribution networks. We show how this model can be used in efficient algorithms for finding the optimal flow configuration in distribution networks, an essential problem in managing power distribution networks. Experiments demonstrate the efficiency and scalability of our approach on publicly available benchmarks and compare favorably against a specialized solver for this task. Our results extend significantly the effectiveness of distributed constraint optimization for practical multi-agent settings

A Personalized System for Conversational Recommendations

Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -- the Adaptive Place Advisor -- that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system

Reusable Knowledge-based Components for Building Software Applications: A Knowledge Modelling Approach

In computer science, different types of reusable components for building software applications were proposed as a direct consequence of the emergence of new software programming paradigms. The success of these components for building applications depends on factors such as the flexibility in their combination or the facility for their selection in centralised or distributed environments such as internet. In this article, we propose a general type of reusable component, called primitive of representation, inspired by a knowledge-based approach that can promote reusability. The proposal can be understood as a generalisation of existing partial solutions that is applicable to both software and knowledge engineering for the development of hybrid applications that integrate conventional and knowledge based techniques. The article presents the structure and use of the component and describes our recent experience in the development of real-world applications based on this approach