Add to ORKGMost AI representations and algorithms for plan generation have not included the concept of informationproducing actions (also called diagnostics, or tests, in the decision making literature). We present a planning representation and algorithm that models information-producing actions and constructs plans that exploit the information produced by those actions
AbstractWe define the probabilistic planning problem in terms of a probability distribution over ini...
Real-world planning problems frequently involve mixtures of continuous and discrete state variables ...
Introduction. Many domains that we wish to model and reason about are subject to change due to the e...
Most AI representations and algorithms for plan gen-eration have not included the concept of informa...
Most AI representations and algorithms for plan generation have not included the concept of informat...
Planning has been one of the main research areas in AI. For about three decades AI researchers explo...
Despite the existence of programs that are able to generate so-called conditional plans, there has y...
In this paper we present some ideas for knowledge representation formalism suitable for rational age...
We investigate the use Markov Decision Processes a.s a means of representing worlds in which action...
As the limitations of traditional AI plan representations have become apparent, researchers have tur...
Abstract: In this paper, we present a software assistant agent that can proactively manage informati...
The goal of the paper is the formulation of a meaningful and practical framework for reasoning about...
hector @ cs.toronto.edu This paper is a very abridged version of one submitted to the 1996 National ...
Human users dealing with multiple objectives in a complex environment, e.g., mili-tary planners or e...
To coordinate with other agents in its environment, an agent needs models of what the other agents a...
AbstractWe define the probabilistic planning problem in terms of a probability distribution over ini...
Real-world planning problems frequently involve mixtures of continuous and discrete state variables ...
Introduction. Many domains that we wish to model and reason about are subject to change due to the e...
Most AI representations and algorithms for plan gen-eration have not included the concept of informa...
Most AI representations and algorithms for plan generation have not included the concept of informat...
Planning has been one of the main research areas in AI. For about three decades AI researchers explo...
Despite the existence of programs that are able to generate so-called conditional plans, there has y...
In this paper we present some ideas for knowledge representation formalism suitable for rational age...
We investigate the use Markov Decision Processes a.s a means of representing worlds in which action...
As the limitations of traditional AI plan representations have become apparent, researchers have tur...
Abstract: In this paper, we present a software assistant agent that can proactively manage informati...
The goal of the paper is the formulation of a meaningful and practical framework for reasoning about...
hector @ cs.toronto.edu This paper is a very abridged version of one submitted to the 1996 National ...
Human users dealing with multiple objectives in a complex environment, e.g., mili-tary planners or e...
To coordinate with other agents in its environment, an agent needs models of what the other agents a...
AbstractWe define the probabilistic planning problem in terms of a probability distribution over ini...
Real-world planning problems frequently involve mixtures of continuous and discrete state variables ...
Introduction. Many domains that we wish to model and reason about are subject to change due to the e...