Intentions in Means-End Planning (Dissertation Proposal)

This proposal discusses the use of the intentions of the actor in performing means-end reasoning. In doing so, it will show that preconditions and applicability conditions in existing systems are ill-defined and intrinsically encode situational information that prevents intentions from playing a role in the planning process. While the former problem can be fixed, the latter cannot. Therefore, I argue that preconditions should be eliminated from action representation. In their place, I suggest explicit representation of intention, situated reasoning about the results of action, and robust failure mechanisms. I then describe a system, the Intentional Planning System (ItPlanS), which embodies these ideas, compare ItPlanS to other systems, and propose future directions for this work

A Reconsideration of Preconditions

This paper is part of an attempt to introduce intentionality of the actor to planning decisions. As a first step in this process the usual representations for actions used by planning systems must be reevaluated. this paper argues for the elimination of preconditions and qualification conditions from action representation in favor of explicit representation of intention, situated reasoning about the results of the action and reactive failure mechanisms. The paper then describes a planning system that has explicit representation and use of intentions and uses action representation that do not have preconditions

A New Model of Plan Recognition

We present a new abductive, probabilistic theory of plan recognition. This model differs from previous plan recognition theories in being centered around a model of plan execution: most previous methods have been based on plans as formal objects or on rules describing the recognition process. We show that our new model accounts for phenomena omitted from most previous plan recognition theories: notably the cumulative effect of a sequence of observations of partially-ordered, interleaved plans and the effect of context on plan adoption. The model also supports inferences about the evolution of plan execution in situations where another agent intervenes in plan execution. This facility provides support for using plan recognition to build systems that will intelligently assist a user.Comment: Appears in Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI1999

Doing What You\u27re Told: Following Task Instructions in Changing, but Hospitable Environments

The AnimNL project (Anim ation from N atural L anguage) has as its goal the automatic creation of animated task simulations from natural-language instructions. The question addressed in this paper is how agents can perform tasks in environments about which they have only partial relevant knowledge. The solution we describe involves enabling such agents to * develop expectations through instruction understanding and plan inference, and use those expectations in deciding how to act; * exploit generalized abilities in order to deal with novel geometric situations. The AnimNL project builds on an animation system, Jack™, that has been developed at the Computer Graphics Research Lab at the University of Pennsylvania, and draws upon a range of recent work in Natural Language semantics, planning and plan inference, philosophical studies of intention, reasoning about knowledge and action, and subsumption architectures for autonomous agents

Planning and Parallel Transition Networks: Animation\u27s New Frontiers

Animating realistic human agents involves more than just creating movements that look real . A principal characteristic of humans is their ability to plan and make decisions based on intentions and the local environmental context. Animated agents must therefore react to and deliberate about their environment and other agents. Our agent animation uses various low-level behaviors, sense-control-action loops, high-level planning, and parallel task networks. Several systems we developed will illustrate how these components contribute to the realism and efficacy of human agent animation

Structural Bootstrapping - A Novel, Generative Mechanism for Faster and More Efficient Acquisition of Action-Knowledge

eISSN: 1943-0612Humans, but also robots, learn to improve their behavior. Without existing knowledge, learning either needs to be explorative and, thus, slow or-to be more efficient-it needs to rely on supervision, which may not always be available. However, once some knowledge base exists an agent can make use of it to improve learning efficiency and speed. This happens for our children at the age of around three when they very quickly begin to assimilate new information by making guided guesses how this fits to their prior knowledge. This is a very efficient generative learning mechanism in the sense that the existing knowledge is generalized into as-yet unexplored, novel domains. So far generative learning has not been employed for robots and robot learning remains to be a slow and tedious process. The goal of the current study is to devise for the first time a general framework for a generative process that will improve learning and which can be applied at all different levels of the robot's cognitive architecture. To this end, we introduce the concept of structural bootstrapping-borrowed and modified from child language acquisition-to define a probabilistic process that uses existing knowledge together with new observations to supplement our robot's data-base with missing information about planning-, object-, as well as, action-relevant entities. In a kitchen scenario, we use the example of making batter by pouring and mixing two components and show that the agent can efficiently acquire new knowledge about planning operators, objects as well as required motor pattern for stirring by structural bootstrapping. Some benchmarks are shown, too, that demonstrate how structural bootstrapping improves performanceTaikomosios informatikos katedraVytauto Didžiojo universiteta

The whole genome sequence of the Mediterranean fruit fly, Ceratitis capitata (Wiedemann), reveals insights into the biology and adaptive evolution of a highly invasive pest species

The Mediterranean fruit fly (medfly), Ceratitis capitata, is a major destructive insect pest due to its broad host range, which includes hundreds of fruits and vegetables. It exhibits a unique ability to invade and adapt to ecological niches throughout tropical and subtropical regions of the world, though medfly infestations have been prevented and controlled by the sterile insect technique (SIT) as part of integrated pest management programs (IPMs). The genetic analysis and manipulation of medfly has been subject to intensive study in an effort to improve SIT efficacy and other aspects of IPM control