Conclusion

The final chapter of the book revisits the emerging Shape Grammar research presented in Chapter 2 and the quantitative analysis and guided generation using a Palladian Grammar in Chapter 3. It then revisits the limitations of Space Syntax approaches and of the justified planning graph (JPG) method and its measures, described in Chapters 4 and 5. Finally, this chapter discusses the new combined grammatical and syntactical method presented in Chapters 6, 7, and 8. This concluding chapter emphasises the book's contribution to advances in Shape Grammar and Space Syntax research for architectural design analysis and generation. In addition to these theoretical contributions, the primary computational approaches in the book, which have been demonstrated using the domestic designs of Palladio, Wright, and Murcutt, are also valuable for architectural education and practice.

A Web Service Composition Approach Based on Planning Graph and Propositional Logic

To improve the computational efficiency of web service automatic composition, this article proposes a novel approach based on a planning graph and propositional logic. This approach has a forward searching stage and a backward combination stage: the former stage searches services in a service storage and performs the composition in a hierarchical architecture based on a planning graph, while the latter stage combines them via propositional logic operations. This method can obtain all composite services that contain no redundant services, and the computational complexity can be significantly reduced. Experiments are done to illustrate the effectiveness of the approach.

Analysis of Attack Graph Representations for Ranking Vulnerability Fixes

Software vulnerabilities in organizational computer networks can be leveraged by an attacker to gain access to sensitive information. As fixing all vulnerabilities requires much effort, it is critical to rank the possible fixes by their importance. Centrality measures over logical attack graphs, or over the network connectivity graph, often provide a scalable method for finding the most critical vulnerabilities.In this paper we suggest an analysis of the planning graph, originating in classical planning, as an alternative for the logical attack graph, to improve the ranking produced by centrality measures. The planning graph also allows us to enumerate the set of possible attack plans, and hence, directly count the number of attacks that use a given vulnerability. We evaluate a set of centrality-based ranking measures over the logical attack graph and the planning graph, showing that metrics computed over the planning graph reduce more rapidly the set of shortest attack plans.

Swarm Automated Planning Algorithm with Repairing Operators

Swarm automated planning algorithm is a planning method based on planning graph technology, and the algorithm which could improve the searching efficiency through importing swarm intelligence has the character of global and parallel. Yet sometimes the algorithm has the shortcoming in the local searching. For instance, maybe the quality of the best candidate planning solution would get lower after another around of searching. To enhance the ability of the local searching and the convergence acceleration, the swarm automated planning algorithm which with the local repairing operators is presented in this paper. The searching efficiency could be bettered through the pertinence repairing operators and the heuristic evaluation information to control the repairing processes.

A Hybrid LP-RPG Heuristic for Modelling Numeric Resource Flows in Planning

Although the use of metric fluents is fundamental to many practical planning problems, the study of heuristics to support fully automated planners working with these fluents remains relatively unexplored. The most widely used heuristic is the relaxation of metric fluents into interval-valued variables --- an idea first proposed a decade ago. Other heuristics depend on domain encodings that supply additional information about fluents, such as capacity constraints or other resource-related annotations. A particular challenge to these approaches is in handling interactions between metric fluents that represent exchange, such as the transformation of quantities of raw materials into quantities of processed goods, or trading of money for materials. The usual relaxation of metric fluents is often very poor in these situations, since it does not recognise that resources, once spent, are no longer available to be spent again. We present a heuristic for numeric planning problems building on the propositional relaxed planning graph, but using a mathematical program for numeric reasoning. We define a class of producer--consumer planning problems and demonstrate how the numeric constraints in these can be modelled in a mixed integer program (MIP). This MIP is then combined with a metric Relaxed Planning Graph (RPG) heuristic to produce an integrated hybrid heuristic. The MIP tracks resource use more accurately than the usual relaxation, but relaxes the ordering of actions, while the RPG captures the causal propositional aspects of the problem. We discuss how these two components interact to produce a single unified heuristic and go on to explore how further numeric features of planning problems can be integrated into the MIP. We show that encoding a limited subset of the propositional problem to augment the MIP can yield more accurate guidance, partly by exploiting structure such as propositional landmarks and propositional resources. Our results show that the use of this heuristic enhances scalability on problems where numeric resource interaction is key in finding a solution.