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..

Applying tropos to socio-technical system design and runtime configuration

Recent trends in Software Engineering have introduced the importance of reconsidering the traditional idea of software design as a socio-tecnical problem, where human agents are integral part of the system along with hardware and software components. Design and runtime support for Socio-Technical Systems (STSs) requires appropriate modeling techniques and non-traditional infrastructures. Agent-oriented software methodologies are natural solutions to the development of STSs, both humans and technical components are conceptualized and analyzed as part of the same system. In this paper, we illustrate a number of Tropos features that we believe fundamental to support the development and runtime reconfiguration of STSs. Particularly, we focus on two critical design issues: risk analysis and location variability. We show how they are integrated and used into a planning-based approach to support the designer in evaluating and choosing the best design alternative. Finally, we present a generic framework to develop self-reconfigurable STSs

Global Risks 2015, 10th Edition.

The 2015 edition of the Global Risks report completes a decade of highlighting the most significant long-term risks worldwide, drawing on the perspectives of experts and global decision-makers. Over that time, analysis has moved from risk identification to thinking through risk interconnections and the potentially cascading effects that result. Taking this effort one step further, this year's report underscores potential causes as well as solutions to global risks. Not only do we set out a view on 28 global risks in the report's traditional categories (economic, environmental, societal, geopolitical and technological) but also we consider the drivers of those risks in the form of 13 trends. In addition, we have selected initiatives for addressing significant challenges, which we hope will inspire collaboration among business, government and civil society communitie

Playing Smart - Artificial Intelligence in Computer Games

Abstract: With this document we will present an overview of artificial intelligence in general and artificial intelligence in the context of its use in modern computer games in particular. To this end we will firstly provide an introduction to the terminology of artificial intelligence, followed by a brief history of this field of computer science and finally we will discuss the impact which this science has had on the development of computer games. This will be further illustrated by a number of case studies, looking at how artificially intelligent behaviour has been achieved in selected games

The Glacier Complexes of the Mountain Massifs of the North-West of Inner Asia and their Dynamics

The subject of this paper is the glaciation of the mountain massifs Mongun-Taiga, Tavan-Boghd-Ola, Turgeni- Nuru, and Harhira-Nuru. The glaciation is represented mostly by small forms that sometimes form a single complex of domeshaped peaks. According to the authors, the modern glaciated area of the mountain massifs is 21.2 km2 (Tavan-Boghd-Ola), 20.3 km2 (Mongun-Taiga), 42 km2 (Turgeni- Nuru), and 33.1 km2 (Harhira-Nuru). The area of the glaciers has been shrinking since the mid 1960’s. In 1995–2008, the rate of reduction of the glaciers’ area has grown considerably: valley glaciers were rapidly degrading and splitting; accumulation of morainic material in the lower parts of the glaciers accelerated. Small glaciers transformed into snowfields and rock glaciers. There has been also a degradation of the highest parts of the glaciers and the collapse of the glacial complexes with a single zone of accumulation into isolated from each other glaciers. Reduced snow cover area has led to a rise in the firn line and the disintegration of a common accumulation area of the glacial complex. In the of the Mongun-Taiga massif, in 1995– 2008, the firn line rose by 200–300 m. The reduction of the glaciers significantly lagged behind the change in the position of the accumulation area boundary. In the past two years, there has been a significant recovery of the glaciers that could eventually lead to their slower degradation or stabilization of the glaciers in the study area