Filtering as a reasoning-control strategy: An experimental assessment

In dynamic environments, optimal deliberation about what actions to perform is impossible. Instead, it is sometimes necessary to trade potential decision quality for decision timeliness. One approach to achieving this trade-off is to endow intelligent agents with meta-level strategies that provide them guidance about when to reason (and what to reason about) and when to act. We describe our investigations of a particular meta-level reasoning strategy, filtering, in which an agent commits to the goals it has already adopted, and then filters from consideration new options that would conflict with the successful completion of existing goals. To investigate the utility of filtering, a series of experiments was conducted using the Tileworld testbed. Previous experiments conducted by Kinny and Georgeff used an earlier version of the Tileworld to demonstrate the feasibility of filtering. Results are presented that replicate and extend those of Kinny and Georgeff and demonstrate some significant environmental influences on the value of filtering

Towards a Principled Representation of Discourse Plans

We argue that discourse plans must capture the intended causal and decompositional relations between communicative actions. We present a planning algorithm, DPOCL, that builds plan structures that properly capture these relations, and show how these structures are used to solve the problems that plagued previous discourse planners, and allow a system to participate effectively and flexibly in an ongoing dialogue.Comment: requires cogsci94.sty, psfig.st

A Unifying Algorithm for Conditional, Probabilistic Planning

Several recent papers describe algorithms for generating conditional and/or probabilistic plans. In this paper, we synthesize this work, and present a unifying algorithm that incorporates and clarifies the main techniques that have been developed in the previous literature. Our algorithm decouples the search-control strategy for conditional and/or probabilistic planning from the underlying plan-refinement process. A similar decoupling has proven to be very useful in the analysis of classical planning algorithms, and we suspect it can be at least as useful here, where the search-control decisions are even more crucial. We describe an extension of conditional, probabilistic planning, to provide candidates for decision-theoretic assessment, and describe the reasoning about failed branches and side-effects that is needed for this purpose

A Problem for RST: The Need for Multi-level Discourse Analysis

this paper, we focus on two levels of analysis. The first involves the relation between the information conveyed in consecutive elements of a coherent discourse. Thus, for example, one utterance may describe an event that can be presumed to be the cause of another event described in the subsequent utterance. This causal relation is at what we will call the informational level. The second level of relation results from the fact that discourses are produced to effect changes in the mental state of the discourse participants. In coherent discourse, a speaker is carrying out a consistent plan to achieve the intended changes, and consecutive discourse elements are related to one another by means of the ways in which they participate in that plan. Thus, one utterance may be intended to increase the likelihood that the hearer will come to * Department of Computer Science and Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA 15260. e-mail: jmoore,pollackcs.pitt.edu 1 In addition, intentional structure is needed to make certain types of choices during the generation process, e.g., how to refer to an object (Appelt 1985

CTP: A New Constraint-Based Formalism for Conditional, Temporal Planning

Temporal constraints pose a challenge for conditional planning, because it is necessary for a conditional planner to determine whether a candidate plan will satisfy the specified temporal constraints. This can be difficult, because temporal assignments that satisfy the constraints associated with one conditional branch may fail to satisfy the constraints along a different branch. In this paper we address this challenge by developing the Conditional Temporal Problem (CTP) formalism, an extension of standard temporal constraint-satisfaction processing models used in non-conditional temporal planning. Specifically, we augment temporal CSP frameworks by (1) adding observation nodes, and (2) attaching labels to all nodes to indicate the situation(s) in which each will be executed. Our extended framework allows for the construction of conditional plans that are guaranteed to satisfy complex temporal constraints. Importantly, this can be achieved even while allowing for decisions about the precise timing of actions to be postponed until execution time, thereby adding flexibility and making it possible to dynamically adapt the plan in response to the observations made during execution. We also show that, even for plans without explicit quantitative temporal constraints, our approach fixes a problem in the earlier approaches to conditional planning, which resulted in their being incomplete.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44793/1/10601_2004_Article_5141764.pd

Using a goal-driven approach to generate test cases for GUIs

The widespread use of GUIs for interacting with soft-ware is leading to the construction of more and more complex GUIs. With the growing complexity comes challenges in testing the correctness of a GUI and the underlying software. We present a new technique to au-tomatically generate test cases for GUIs that exploits planning, a well developed and used technique in ar-tificial intelligence. Given a set of operators, an initial state and a goal state, a planner produces a sequence of the operators that will change the initial state to the goal state. Our test case generation technique first ana-lyzes a GUI and derives hierarchical planning operators from the actions in the GUI. The test designer deter-mines the preconditions and effects of the hierarchical operators, which are then input into a planning system. With the knowledge of the GUI and the way in which the user will interact with the GUI, the test designer creates sets of initial and goal states. Given these ini-tial and final states of the GUI, a hierarchical planner produces plans, or a set of test cases, that enable the goal state to be reached. Our technique has the ad-ditional benefit of putting verification commands into the test cases automatically. We implemented our tech-nique by developing the GUI analyzer and extending a planner. We generated test cases for Microsoft’s Word-Pad to demonstrate the viability and practicality of the approach

Multi-format Notifications for Multi-tasking

Abstract. We studied people's perception of and response to a set of visual and auditory notifications issued in a multi-task environment. Primary findings show that participants' reactive preference ratings of notifications delivered in various contexts during experimentation appear to contradict their reflective, overall ratings of the notification formats when elicited independently of contextual information, indicating a potential difficulty in people's abilities to articulate their preferences in the absence of context. We also found people to vary considerably in their preferences for different notification formats delivered in different contexts, such taht simple approaches to selecting notification delivery formats will be dissatisfying to users a substantial portion of the time. These findings can inform the designs of future systems: rather than target the general user alone, they should strive to better understand each user individually

Decomposition and Causality in Partial-order Planning

We describe DPOCL, a partial-order causal link planner that includes action decomposition

Computing: report leaps geographical barriers but stumbles over gender

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62917/1/441025a.pd

Clinical decision-making: physicians' preferences and experiences

BACKGROUND: Shared decision-making has been advocated; however there are relatively few studies on physician preferences for, and experiences of, different styles of clinical decision-making as most research has focused on patient preferences and experiences. The objectives of this study were to determine 1) physician preferences for different styles of clinical decision-making; 2) styles of clinical decision-making physicians perceive themselves as practicing; and 3) the congruence between preferred and perceived style. In addition we sought to determine physician perceptions of the availability of time in visits, and their role in encouraging patients to look for health information. METHODS: Cross-sectional survey of a nationally representative sample of U.S. physicians. RESULTS: 1,050 (53% response rate) physicians responded to the survey. Of these, 780 (75%) preferred to share decision-making with their patients, 142 (14%) preferred paternalism, and 118 (11%) preferred consumerism. 87% of physicians perceived themselves as practicing their preferred style. Physicians who preferred their patients to play an active role in decision-making were more likely to report encouraging patients to look for information, and to report having enough time in visits. CONCLUSION: Physicians tend to perceive themselves as practicing their preferred role in clinical decision-making. The direction of the association cannot be inferred from these data; however, we suggest that interventions aimed at promoting shared decision-making need to target physicians as well as patients