Summarizing and Comparing Story Plans

Branching story games have gained popularity for creating unique playing experiences by adapting story content in response to user actions. Research in interactive narrative (IN) uses automated planning to generate story plans for a given story problem. However, a story planner can generate multiple story plan solutions, all of which equally-satisfy the story problem definition but contain different story content. These differences in story content are key to understanding the story branches in a story problem\u27s solution space, however we lack narrative-theoretic metrics to compare story plans. We address this gap by first defining a story plan summarization model to capture the important story semantics from a story plan. Secondly, we define a story plan comparison metric that compares story plans based on the summarization model. Using the Glaive narrative planner and a simple story problem, we demonstrate the usefulness of using the summarization model and distance metric to characterize the different story branches in a story problem\u27s solution space

Qualitative and Quantitative Solution Diversity in Heuristic-Search and Case-Based Planning

Planning is a branch of Artificial Intelligence (AI) concerned with projecting courses of actions for executing tasks and reaching goals. AI Planning helps increase the autonomy of artificially intelligent agents and decrease the cognitive load burdening human planners working in challenging domains, such as the Mars exploration projects. Approaches to AI planning include first-principles heuristic search planning and case-based planning. The former conducts a heuristic-guided search in the solution space, while the latter generates new solutions by adapting solutions to previously-solved problems.The ability to generate not just one solution, but a set of meaningfully diverse solutions to each planning problem helps cater to a wider variety of user preferences and needs (which it may be difficult or even unfeasible to acquire and/or represent in their entirety), produce viable alternative courses of action to fall back on in case of failure, counter varied threats in intrusion detection, render computer games more compelling, and provide representative samples of the vast search spaces of planning problems.This work describes a general framework for generating diverse sets of solutions (i.e. courses of action) to planning problems. The general diversity-aware planning algorithm consists of iteratively generating solutions using a composite candidate-solution evaluation criterion taking into account both how promising the candidate solutions appear in their own right and on how likely they are to increase the overall diversity of the final set of solutions. This estimate of diversity is based on distance metrics, i.e. measures of the dissimilarity between two solutions. Distance metrics can be quantitative or qualitative.Quantitative distance measures are domain-independent. They require minimum knowledge engineering, but may not reflect dissimilarities that are truly meaningful. Qualitative distance metrics are domain-specific and reflect, based on the domain knowledge encoded within them, the kind of meaningful dissimilarities that might be identified by a person familiar with the domain.Based on the general framework for diversity-aware planning, three domain-independent planning algorithms have been implemented and are described and evaluated herein. DivFF is a diverse heuristic search planner for deterministic planning domains (i.e. domains for which the assumption is made that any action can only have one possible outcome). DivCBP is a diverse case-based planner, also for deterministic planning domains. DivNDP is a heuristic search planner for nondeterministic planning domains (i.e. domains the descriptions of which include actions with multiple possible outcomes). The experimental evaluation of the three algorithms is conducted on a computer game domain, chosen for its challenging characteristics, which include nondeterminism and dynamism. The generated courses of action are run in the game in order to ascertain whether they affect the game environment in diverse ways. This constitutes the test of their genuine diversity, which cannot be evaluated accurately based solely on their low-level structure.It is shown that all proposed planning systems successfully generate sets of diverse solutions using varied criteria for assessing solution dissimilarity. Qualitatively-diverse solution sets are demonstrated to constantly produce more diverse effects in the game environment than quantitatively-diverse solution sets.A comparison between the two planning systems for deterministic domains, DivCBP and DivFF, reveals the former to be more successful at consistently generating diverse sets of solutions. The reasons for this are investigated, thus contributing to the literature of comparative studies of first-principles and case-based planning approaches. Finally, an application of diversity in planning is showcased: simulating personality-trait variation in computer game characters. Sets of diverse solutions to both deterministic and nondeterministic planning problems are shown to successfully create diverse character behavior in the evaluation environment

Network-centric automated planning and execution

Web services provide interoperability to network hosts with different capabilities. Complex tasks can be performed by composing services, assuming sufficient service descriptions are provided. Researchers are just beginning to realize the importance of accounting for network properties during automated service composition. The work presented in this thesis considers dynamic, heterogeneous networks—one type of network-centric environment.The purpose of this research is to improve network-centric service composition. This is accomplished by converting the service composition problem to an automated planning under uncertainty problem and by reasoning about network properties at various stages of the planning process. This thesis presents a method of improving the agents’ ability to construct, execute, and monitor plans in network-centric environments.There are two main contributions of this thesis: 1) generating qualitatively-different plans and 2) creating network-aware agents. As part of the former contribution, this thesis presents a comparison of methods used to create classical planning domains for distributed service composition problems. The other part of this contribution is an algorithm for guiding a plan-space planner to create qualitatively-different plans based on domain-dependent and network-centric plan evaluations. The second contribution pertains to network-awareness, which agents exhibit by reacting to changes in network conditions. This thesis describes methods of incorporating network-awareness into agents that 1) create plans, 2) execute plans, and 3) monitor plan execution.Experiments to validate the aforementioned contributions are presented in the context of an Improvised Explosive Device (IED) detection scenario. Several locations are monitored for IEDs using a variety of techniques including manual searching and visual change detection, as well as a variety of resources including humans, robots, and unmanned aerial vehicles (UAVs). Empirical results indicate that incorporating network-awareness into agents in dynamic, heterogeneous networks improves the overall service composition performance and effectiveness.M.S., Computer Science -- Drexel University, 200

Metatheoretic Plan Summarization and Comparison

We describe a domain-independent framework for plan summarization and comparison that can help a human understand both key strategic elements of an individual plan and important differences among plans. Our approach is grounded in a domain metatheory, which specifies important semantic properties of tasks, instances and planning methods. The metatheory provides a semantic framework for guiding the choice and description of concepts used in summarizing and comparing plans, thus avoiding syntactic constructs whose meaning or import is unclear. We define three capabilities grounded in the metatheoretic approach: (a) summarization of an individual plan, (b) comparison of pairs of plans, and (c) analysis of a collection of plans. Application of these capabilities within a rich application domain shows their value in facilitating user understandability of complex plans

Metatheoretic Plan Summarization and Comparison

We describe a domain-independent framework for plan summarization and comparison that can help a human understand both the key elements of an individual plan and important differences among plans. Our approach is grounded in the use of a domain metatheory, which is an abstract characterization of a planning domain that specifies important semantic properties of templates, planning variables, and instances. The metatheory provides a semantic framework for guiding the choice and description of concepts used in summarizing and comparing plans, thus enabling results that are grounded in semantically significant concepts rather than syntactic constructs whose meaning or import is unclear. We define three specific capabilities grounded in the metatheoretic approach: (a) summarization of an individual plan, (b) comparison of pairs of plans, and (c) analysis of a collection of plans. Use of these capabilities within a rich application domain shows their value in facilitating the understandability of complex plans by a user