Proof Generation from Delta-Decisions

We show how to generate and validate logical proofs of unsatisfiability from delta-complete decision procedures that rely on error-prone numerical algorithms. Solving this problem is important for ensuring correctness of the decision procedures. At the same time, it is a new approach for automated theorem proving over real numbers. We design a first-order calculus, and transform the computational steps of constraint solving into logic proofs, which are then validated using proof-checking algorithms. As an application, we demonstrate how proofs generated from our solver can establish many nonlinear lemmas in the the formal proof of the Kepler Conjecture.Comment: Appeared in SYNASC'1

Pathfinding in Games

Commercial games can be an excellent testbed to artificial intelligence (AI) research, being a middle ground between synthetic, highly abstracted academic benchmarks, and more intricate problems from real life. Among the many AI techniques and problems relevant to games, such as learning, planning, and natural language processing, pathfinding stands out as one of the most common applications of AI research to games. In this document we survey recent work in pathfinding in games. Then we identify some challenges and potential directions for future work. This chapter summarizes the discussions held in the pathfinding workgroup

Bridging the gap between business process models and service-oriented architectures with reference to the grid environment

In recent years, organisations have been seeking technological solutions for enacting their business process models using ad-hoc and heuristic approaches. However, limited results have been obtained due to the expansion of business processes across geographical boundaries and the absence of structured methods, frameworks and/or Information Technology (IT) infrastructures to enact these processes. In an attempt to enact business process models using distributed technologies, we introduce a novel architectural framework to bridge the gap between business process models and Grid-aware Service-Oriented Architectures (GSOA). BPMSOA framework is aligned with the Model-Driven Engineering (MDE) approach and is instantiated for role-based business process models [in particular Role Activity Diagramming (RAD)], using mobile process languages such as pi-ADL. The evaluation of the BPMSOA framework using the Submission process from the digital libraries domain has revealed that role-based business process models can be successfully enacted in GSOA environments with certain limitations. © 2011 Inderscience Enterprises Ltd

Recovering Trace Links Between Software Documentation And Code

Introduction Software development involves creating various artifacts at different levels of abstraction and establishing relationships between them is essential. Traceability link recovery (TLR) automates this process, enhancing software quality by aiding tasks like maintenance and evolution. However, automating TLR is challenging due to semantic gaps resulting from different levels of abstraction. While automated TLR approaches exist for requirements and code, architecture documentation lacks tailored solutions, hindering the preservation of architecture knowledge and design decisions. Methods This paper presents our approach TransArC for TLR between architecture documentation and code, using componentbased architecture models as intermediate artifacts to bridge the semantic gap. We create transitive trace links by combining the existing approach ArDoCo for linking architecture documentation to models with our novel approach ArCoTL for linking architecture models to code. Results We evaluate our approaches with five open-source projects, comparing our results to baseline approaches. The model-to-code TLR approach achieves an average F1-score of 0.98, while the documentation-to-code TLR approach achieves a promising average F1-score of 0.82, significantly outperforming baselines. Conclusion Combining two specialized approaches with an intermediate artifact shows promise for bridging the semantic gap. In future research, we will explore further possibilities for such transitive approaches

Recovering Trace Links Between Software Documentation And Code

Introduction Software development involves creating various artifacts at different levels of abstraction and establishing relationships between them is essential. Traceability link recovery (TLR) automates this process, enhancing software quality by aiding tasks like maintenance and evolution. However, automating TLR is challenging due to semantic gaps resulting from different levels of abstraction. While automated TLR approaches exist for requirements and code, architecture documentation lacks tailored solutions, hindering the preservation of architecture knowledge and design decisions. Methods This paper presents our approach TransArC for TLR between architecture documentation and code, using componentbased architecture models as intermediate artifacts to bridge the semantic gap. We create transitive trace links by combining the existing approach ArDoCo for linking architecture documentation to models with our novel approach ArCoTL for linking architecture models to code. Results We evaluate our approaches with five open-source projects, comparing our results to baseline approaches. The model-to-code TLR approach achieves an average F1-score of 0.98, while the documentation-to-code TLR approach achieves a promising average F1-score of 0.82, significantly outperforming baselines. Conclusion Combining two specialized approaches with an intermediate artifact shows promise for bridging the semantic gap. In future research, we will explore further possibilities for such transitive approaches

Fiat: Deductive Synthesis of Abstract Data Types in a Proof Assistant

We present Fiat, a library for the Coq proof assistant supporting refinement of declarative specifications into efficient functional programs with a high degree of automation. Each refinement process leaves a proof trail, checkable by the normal Coq kernel, justifying its soundness. We focus on the synthesis of abstract data types that package methods with private data. We demonstrate the utility of our framework by applying it to the synthesis of query structures--abstract data types with SQL-like query and insert operations. Fiat includes a library for writing specifications of query structures in SQL-inspired notation, expressing operations over relations (tables) in terms of mathematical sets. This library includes a suite of tactics for automating the refinement of specifications into efficient, correct- by-construction OCaml code. Using these tactics, a programmer can generate such an implementation completely automatically by only specifying the equivalent of SQL indexes, data structures capturing useful views of the abstract data. Throughout we speculate on the new programming modularity possibilities enabled by an automated refinement system with proved-correct rules. “Every block of stone has a statue inside it and it is the task of the sculptor to discover it.”--MichelangeloNational Science Foundation (U.S.) (NSF grant CCF-1253229)United States. Defense Advanced Research Projects Agency (DARPA, agreement number FA8750-12-2- 0293

Requirements engineering for business workflow systems: a scenario-based approach

Workflow implementations require a deep understanding of business and human cooperation. Several approaches have been proposed to address this need for understanding, but largely in a descriptive way. Attempts to use them in software development have had mixed results. The work reported here proposes that these approaches can be used in a generative way, as part of the requirement engineering process, by (a) extending requirements engineering modelling techniques with underlying cooperation properties, (b) integrating these techniques through the use of a derivation modelling approach, and (c) providing pragmatic heuristics and guidelines that support the real-world requirements engineering practitioner to ensure a high probability of success for the business workflow system to be developed. This thesis develops and evaluates a derivation modelling approach that is based on scenario modelling. It supports clear and structured views of cooperation properties, and allows the derivation of articulation protocols from business workflow models in a scenario-driven manner. This enables requirements engineering to define how the expectations of the cooperative situation are to be fulfilled by the system to be built - a statement of requirements for business workflow systems that reflects the richness of these systems, but also acts as a feasible starting point for development. The work is evaluated through a real-world case study of business workflow management. The main contribution of this work is a demonstration that the above problems in modelling requirements for business workflow systems can be addressed by scenario-based derivation modelling approach. The method transforms models through a series of properties involving cooperation, which can be addressed by using what are effectively extensions of current requirements engineering methods

Discovering User-Interpretable Capabilities of Black-Box Planning Agents

Several approaches have been developed for answering users' specific questions about AI behavior and for assessing their core functionality in terms of primitive executable actions. However, the problem of summarizing an AI agent's broad capabilities for a user is comparatively new. This paper presents an algorithm for discovering from scratch the suite of high-level "capabilities" that an AI system with arbitrary internal planning algorithms/policies can perform. It computes conditions describing the applicability and effects of these capabilities in user-interpretable terms. Starting from a set of user-interpretable state properties, an AI agent, and a simulator that the agent can interact with, our algorithm returns a set of high-level capabilities with their parameterized descriptions. Empirical evaluation on several game-based scenarios shows that this approach efficiently learns descriptions of various types of AI agents in deterministic, fully observable settings. User studies show that such descriptions are easier to understand and reason with than the agent's primitive actions.Comment: KR 202