Igor is a knowledge-based system for exploratory statistical analysis of complex data. Igor has two related goals: to help automate the search for interesting patterns in data sets, and to help develop models that capture significant relationships in the data. Igor relies on a planning representation to manage the complexities of data analysis. We outline the relationship between planning and E...

We propose a formal theory to capture qualitatively the characteristics of utility-relevance and effect-uncertainty of chance. It sets a definite boundary between chance discovery and classical artificial intelligence planning: the former pursues partial realization of desire while the latter complete realization. The theory also provides a formal specification and deriving machinery for partia...

Sequential control is a common control problem in industry. Despite its importance fairly little theoretical research has been devoted to this problem. We study a subclass of sequential control problems, which we call the SAS-PUBS class, and present a planning algorithm for this class. The algorithm is developed using formalism from artiicial intelligence (AI). For planning problems in the SAS-...

Multi-agent systems are beginning to play an important role in today’s software development. This competition is an attempt to stimulate research in the area of multi-agent systems by identifying key problems and collecting suitable benchmarks that can serve as milestones for testing new approaches and techniques from computational logics. While there exist several competitions in various parts...

Recent years has seen intense analysis and questioning of the importance of the role of classical AI-planning in deciding moment-to-moment actions. This has led to development of several new AI-planning paradigms such as reactive planning [Fir87, GL87]. Informally, a planning paradigm refers to principles ofrepresenting an agent's acts, beliefs and expectations about concepts-relatingacts-to-co...