this article, we focus on the ideas underlying the softbot-based interface.

We present zeno, a least commitment planner that handles actions occurring over extended intervals of time. Deadline goals, metric preconditions, metric effects, and continuous change are supported. Simultaneous actions are allowed when their effects do not interfere. Unlike most planners that deal with complex languages, the zeno planning algorithm is sound and complete. The running code is a complete implementation of the formal algorithm, capable of solving simple problems (i.e., those involving less than a dozen steps). Introduction We have built a least commitment planner, zeno, that handles actions occuring over extended intervals of time and whose preconditions and effects can be temporally quantified. These capabilities enable zeno to reason about deadline goals, piecewise-linear continuous change, external events and to a limited extent, simultaneous actions. While other planners exist with some of these features, zeno is different because it is both sound and complete. As a...

This paper challenges the prevailing pessimism about the scalability of partial order planning (POP) algorithms by presenting several novel heuristic control techniques that make them competitive with the state of the art plan synthesis algorithms. Our key insight is that the techniques responsible for the efficiency of the currently successful planners--viz., distance based heuristics, reachability analysis and disjunctive constraint handling--can also be adapted to dramatically improve the efficiency of the POP algorithm. We implement our ideas in a variant of UCPOP called REPOP # . Our empirical results show that in addition to dominating UCPOP, REPOP also convincingly outperforms Graphplan in several "parallel" domains. The plans generated by REPOP also tend to be better than those generated by Graphplan and state search planners in terms of execution flexibility. 1

Classical planners have traditionally made the closed world assumption --- facts absent from the planner's world model are false. Incompleteinformation planners make the open world assumption --- the truth value of a fact absent from the planner's model is unknown, and must be sensed. The open world assumption leads to two difficulties: (1) How can the planner determine the scope of a universally quantified goal? (2) When is a sensory action redundant, yielding information already known to the planner? This paper describes the fully-implemented xii planner, which solves both problems by representing and reasoning about local closed world information (LCW). We report on experiments utilizing our UNIX softbot (software robot) which demonstrate that LCW can substantially improve the softbot 's performance by eliminating redundant information gathering. Introduction Classical planners (e.g., (Chapman 1987)) presuppose correct and complete information about the world. Although recent wo...

In this paper, we present an integrated planning and robotic architecture that actively directs an agent engaged in an urban search and rescue (USAR) scenario. We describe three salient features that comprise the planning component of this system, namely (1) the ability to plan in a world open with respect to objects, (2) execution monitoring and replanning abilities, and (3) handling soft goals, and detail the interaction of these parts in representing and solving the USAR scenario at hand. We show that though insufficient in an individual capacity, the integration of this trio of features is sufficient to solve the scenario that we present. We test our system with an example problem that involves soft and hard goals, as well as goal deadlines and action costs, and show via an included video that the planner is capable of incorporating sensing actions and execution monitoring in order to produce goal-fulfilling plans that maximize the net benefit accrued.

Abstract. AI planning has featured in a number of Interactive Storytelling prototypes: since narratives can be naturally modelled as a sequence of actions it has been possible to exploit state of the art planners in the task of narrative generation. However the characteristics of a “good ” plan, such as optimality, aren’t necessarily the same as those of a “good ” narrative, where errors and convoluted sequences may offer more reader interest, so some narrative structuring is required. In our work we have looked at injecting narrative control into plan generation through the use of PDDL3.0 state trajectory constraints which enable us to express narrative control information within the planning representation. As part of this we have developed an approach to planning with such trajectory constraints. The approach decomposes the problem into a set of smaller subproblems using the temporal orderings described by the constraints and then solves these subproblems incrementally. In this paper we outline our method and present results that illustrate the potential of the approach. 1

Proof planning is an alternative methodology to classical automated theorem proving based on exhaustive search that was first introduced by Bundy [8]. The goal of this paper is to extend the current realm of proof planning to cope with genuinely mathematical problems such as the well-known limit theorems first investigated for automated theorem proving by Bledsoe. The report presents a general methodology and contains ideas that are new for proof planning and theorem proving, most importantly ideas for search control and for the integration of domain knowledge into a general proof planning framework. We extend proof planning by employing explicit control-rules and supermethods. We combine proof planning with constraint solving.

In recent times, improvements in imaging technology have made available an incredible array of dormation in image format. While powerful and sophisticated image processing software tools are available to prepare and analyze the data, these tools are complex and cumbersome, requiring significant expertise to properly operate. Thus, in order to extract (e.g., mine or analyze) useful information from the data, a user (in our case a scientist) often must possess both significant science and image processing expertise. fis article is an extended version of [8] and describes the use of AI planning techniques to represent scientific, image processing, and software tool knowledge to automate knowledge discovery and data mining (e.g., science data analysis) of large image databases. In particular, we describe two fielded systems. The Multimission VICAR Planner (MVP) which has been deployed for since 1995 years and is currently supporting science product generation for the Galileo mission. MVP has reduced time to fill certain classes of requests from 4 hours to 15 minutes. The Automated SAR Image Processing system (ASIP) was deployed at the Dept. of Geology at Arizona State University in 1997 to support aeolian science analysis of synthetic aperture radar images. ASIP reduces the number of manual inputs in science product generation by 10-fold.