Plan generation using a method of deductive program synthesis

Synthesis and transformation of logic programs using unfold/fold proofs

We present a method for example-guided synthesis of functional programs over recursive data structures. Given a set of input-output examples, our method synthesizes a program in a functional language with higher-order combinators like map and fold. The synthesized program is guaranteed to be the simplest program in the language to fit the examples. Our approach combines three technical ideas: inductive generalization, deduction, and enumerative search. First, we generalize the input-output examples into hypotheses about the structure of the target program. For each hypothesis, we use deduction to infer new input/output examples for the missing subexpressions. This leads to a new subproblem where the goal is to synthesize expressions within each hypothesis. Since not every hypothesis can be realized into a program that fits the examples, we use a combination of best-first enumeration and deduction to search for a hypothesis that meets our needs. We have implemented our method in a tool called λ2, and we evaluate this tool on a large set of synthesis problems involving lists, trees, and nested data structures. The experiments demonstrate the scalability and broad scope of λ2. A highlight is the synthesis of a program believed to be the world's earliest functional pearl.

Program Synthesis, which is the task of discovering programs that realize user intent, can be useful in several scenarios: enabling people with no programming background to develop utility programs, helping regular programmers automatically discover tricky/mundane details, program understanding, discovery of new algorithms, and even teaching.

We view logic programs as (first-order) axiomatisations, and deductive synthesis and transformation as operations on mathematical systems composed of axioms and “logical” systems such as SLD and SLDNF. In this framework, we distinguish between specification, deductive synthesis and transformation and discuss their inter-relationships.

The goal of the new edition is to continue with a systems view of the world. For a more robust and worldwide market dissemination, the new edition has changed to a reference book. The project systems approach to project management, is needed in executing projects across countries and across cultures, which is a crucial requirement in today's globalized and intertwined economics. The book uses ample graphical representations to clarify the concepts and techniques presented. The case examples help to reinforce the topics covered. Several illustrative examples and practice exercises are included. Each chapter is updated and new chapters include Project Simulation and Project Templates. A new chapter on managing complex projects in an age of artificial intelligence adds a unique value to the book. Features Highlights contemporary best practices of project management Uses a systems framework to integrate quantitative and qualitative tools Offers illustrative examples and practice exercises Covers project schedule performance appraisal techniques Discusses the knowledge areas contained in the Project Management Book of Knowledge (PMBOK) Presents software applications for project management, as well as case examples

We describe the formal specification and automated synthesis of a strategic airlift scheduler for the Air Mobility Command of the US Air Force. The program synthesis system, the Kestrel Interactive Development System, composes a formal domain theory with a formal description of a class of algorithms (global search with constraint propagation) to produce provably correct and highly efficient code that outperforms more conventional approaches to this scheduling problem.

Landowner characteristics influence his/her willingness to change landuse practices to provide more or less environmental benefits. However, most studies of agricultural/environmental polices identify landowners as homogenous. And, the primary cause of failure of many environmental and other polices is the lack of knowledge on how humans may respond to polices based on changes in their behavior (Stern, 1993). From socioeconomic theory and empirical research, landowners can be identified as individuals who make agricultural landuse decisions independently based on their objectives. Identifying possible classes of landowners, assessing how each would potentially respond to policy alternatives, and the resulting pattern of land uses in a watershed or a riparian corridor would be very useful to policy makers as they evaluated alternatives. Agricultural landscapes are important producers of ecosystem services. The mix of ecosystem services and commodity outputs of an agricultural landscape depends on the spatial pattern of land uses emerging from individual land use decisions. However, many empirical studies show that the production of ecosystem services from agricultural landscapes is declining. This is consistent with research conducted over the last few decades showing there is a narrow range of social circumstances under which landowners are willing to make investments in the present to achieve public benefits in the future through investing in natural capital resulting in public goods which are frequently produced as ecosystem services. In this study an agent-based model within a watershed planning context is used to analyze the tradeoffs involved in producing a number of ecosystem services and agricultural commodities given price and policy scenarios while assuming three different types of agents in terms of their goals. The agents represent landowners who have been divided into a number of different groups based on their goals and the size of their farm operations. The multi-agent-based model is developed using a heuristic search and optimization technique called genetic algorithm (GA) (Holland), which belongs to a broader class of evolutionary algorithms. GAs exhibit three properties (1) they start with a population of solution, (2) they explore the solution space through recombination and mutation and (3) they evaluate individual solutions based on their appropriate fitness value(s), for example given profit maximizing agents this would be gross margin. A GA is a heuristic stochastic search and optimization method, which works by mimicking the evolutionary principles and chromosomal processing in natural genetics. The three economic agents that are modeled are based on variations in their objective functions and constraints. This study will help in identifying the tradeoffs associated with various agents in the provision of ecosystem services and agricultural commodities. The agent model developed here will help policy and decision maker identify the various agents within the watershed and assess various policy options based on that information. The study will also help to understand the interaction and feedback between the agents and their environment associated with various policy initiatives. The results of the study indicate that the agent model correctly predicts the actual landuse landcover map by 75 percent. Key words: Multifunctional agriculture, Agent based modeling, Genetic Algorithm.

Metamorphic malware are self-modifying programs which apply semantic preserving transformations to their own code in order to foil detection systems based on signature matching. Metamorphism impacts both software security and code protection technologies: it is used by malware writers to evade detection systems based on pattern matching and by software developers for preventing malicious host attacks through software diversification. In this paper, we consider the problem of automatically extracting metamorphic signatures from the analysis of metamorphic malware variants. We define a metamorphic signature as an abstract program representation that ideally captures all the possible code variants that might be generated during the execution of a metamorphic program. For this purpose, we developed MetaSign: a tool that takes as input a collection of metamorphic code variants and produces, as output, a set of transformation rules that could have been used to generate the considered metamorphic variants. MetaSign starts from a control flow graph representation of the input variants and agglomerates them into an automaton which approximates the considered code variants. The upper approximation process is based on the concept of widening automata, while the semantic preserving transformation rules, used by the metamorphic program, can be viewed as rewriting rules and modeled as grammar productions. In this setting, the grammar recognizes the language of code variants, while the production rules model the metamorphic transformations. In particular, we formalize the language of code variants in terms of pure context-free grammars, which are similar to context-free grammars with no terminal symbols. After the widening process, we create a positive set of samples from which we extract the productions of the grammar by applying a learning grammar technique. This allows us to learn the transformation rules used by the metamorphic engine to generate the considered code variants. We validate the results of MetaSign on some case studies.

As a powerful and object-oriented graphical modeling language, Unified Modeling Language (UML) has been introduced into the design and development of safety-critical computer systems. However, UML model is difficult to reflect the concurrency and consistency of constraint rules between objects and impossible to completely demonstrate the dynamic behavior characteristics of a system. Further, it cannot be directly expressed and analyzed by mathematical tools. Petri net modeling technology uses graphical element place and transition to clearly describe the internal interactions of a system. Meanwhile, Petri net has mature mathematical analysis methods. We have proposed to combine these two tools for designing and analyzing a system with a set of transformation rules from UML statecharts to Petri net. In this paper, an automatic conversion tool is introduced. The tool performs the transformation with three steps as follows: (1) Transforming UML model into XML document, which makes information extraction of the UML model become easier. (2) Analyzing semantics of UML model and Petri net model, then designing data structure for transformation, and completing the transformation based on the transformation rules. (3) Analyzing the transformed Petri net model to verify the characteristics of the UML model. The transformation rules and the tools are developed based on a new railway interlocking system.

Diabetes mellitus is a well-known metabolic disorder with numerous complications, such as macrovascular diseases (e.g., coronary heart disease, diabetic cardiomyopathy, stroke, and peripheral vascular disease), microvascular diseases (e.g., diabetic nephropathy, retinopathy, and diabetic cataract), and neuropathy. Multiple contributing factors are implicated in these complications, and the accumulation of oxidative stress is one of the critical ones. Several lines of evidence have suggested that oxidative stress may induce epigenetic modifications that eventually contribute to diabetic vascular complications. As one kind of epigenetic regulator involved in various disorders, non-coding RNAs have received great attention over the past few years. Non-coding RNAs can be roughly divided into short (such as microRNAs; ~21–25 nucleotides) or long non-coding RNAs (lncRNAs; >200 nucleotides). In this review, we briefly discussed the research regarding the roles of various lncRNAs, such as MALAT1, MEG3, GAS5, SNHG16, CASC2, HOTAIR, in the development of diabetic vascular complications in response to the stimulation of oxidative stress.

Synthesizing intended programs from user-specified input-output examples, also known as Programming by Examples (PBE), is a challenging problem in program synthesis, and has been applied to a wide range of domains. A key challenge in PBE is to efficiently discover a user-intended program in the search space that can be exponentially large. In this work, we propose a method for automatic synthesis of functional programs on list manipulation, by using offline-trained Recurrent Neural Network (RNN) models to guide the program search. We adopt miniKanren, an embedded domain-specific language for flexible relational programming, as an underlying top-down deductive search engine of candidate programs that are consistent with input-output examples. Our approach targets an easy and effective integration of deep learning techniques in making better PBE systems and combines two technical ideas on generating diverse training dataset and designing rich feature embeddings of probable subproblems for synthesis generated by deductive search. The offline-learned model is then used in PBE to guide the top-down deductive search with specific strategies. To practically manipulate data structures of lists, our method synthesizes functional programs with popular higher-order combinators including $\texttt {map}$ , $\texttt {foldl}$ and $\texttt {foldr}$ . We have implemented our method and evaluated it with challenging program synthesis tasks on list manipulation. The experiments show promising results on the performance of our method compared to related state-of-the-art inductive synthesizers.

This paper addresses a multi-objective approach for simultaneous dynamic expansion planning of conventional sub-transmission grid and Regional Virtual Power Plant (RVPP). In such a feed-in tariff electricity market, it will assist RVVP’s stakeholders in deciding whether or not they should invest in new equipment’s installation. A Multi-Objective Particle Swarm Optimization (MOPSO) algorithm as a heuristic optimization method is proposed to eliminate the conventional centralized planning, which has led to competition between Regional Electric Company (REC) and RVPP for power delivery in sub-transmission system. Two objective functions are considered for simultaneous expansion planning of these two systems. The first one takes the minimum cost of the REC, as the sub-transmission grid operator, into account while the other one considers profit maximization for RVPP. To achieve the goals, MOPSO algorithm is employed to find the best expansion of REC and best location and capacity of RVPP’s resources. Having solved the proposed multi-objective optimization problem, a Pareto front is determined to show the trade-off between REC and RVPPs' contributions in joint optimal expansion planning of conventional sub-transmission grid and the RVPP. To demonstrate the applicability and effectiveness of the proposed approach, a realistic sub-transmission system in Guilan Province, Iran is used as a test system, and the results are evaluated accordingly.

Since the emergence of Artificial Intelligence as a field, planning under uncertainty has been viewed as one of its crucial subareas. Accordingly, the current lack of scalability of probabilistic planning techniques is a major reason why the grand vision of AI has not been fulfilled yet. Besides being an obstacle to advances in AI, scalability issues also hamper the applicability of probabilistic planning to real-world problems. A powerful framework for describing probabilistic planning problems is Markov Decision Processes (MDPs). Informally, an MDP specifies the objectives the agent is trying to achieve, the actions the agent can perform, and the states in which it can end up while working towards the objective. Solving an MDP means finding a policy, i.e. an assignment of actions to states, that allows the agent to achieve the objective. Optimal solution methods, those that look for the “best” policy according to some criterion, typically try to analyze all possible states or a large fraction of them. Since state space sizes of realistic scenarios can be astronomical, these algorithms quickly run out of memory. Fortunately, the mathematical structure of some classes of MDPs has allowed for inventing more efficient algorithms. This is the case for Stochastic Shortest Path (SSP) problems, whose mathematical properties gave rise to a family of algorithms called Find-and-Revise. When used in combination with a heuristic, the members of this family can find a near-optimal, or even optimal, policy while avoiding the analysis of many states. Nonetheless, the sheer number of states in real-world problems forces algorithms based on state-level analysis to exhaust their capabilities much too early, calling for a fundamentally different approach. Moreover, several expressive and potentially useful MDP classes are currently not known to have a mathematical structure for creating efficient approximation techniques. This dissertation advances the state of the art in probabilistic planning in three complementary ways. For SSP MDPs, it derives a novel class of approximate algorithms based on generalizing state analysis information across many states. This information is stored in the form of basis functions that are generated automatically and the number of which is much smaller than the number of states. As a result, the proposed algorithms have a very compact memory footprint and arrive at high-quality solutions faster than their state-based counterparts. In a parallel effort, the dissertation also builds up mathematical apparatus for classes of MDPs that previously had no applicable approximation algorithms. The developed theory enables the extension of the powerful Find-and-Revise paradigm to these MDP classes as well, providing the first memory-efficient algorithms for solving them. Last but not least, the dissertation will apply the proposed theoretical techniques to a large-scale real-world problem, urban traffic routing being one of the candidates.

This thesis proposes, analyzes and tests different exploration-based techniques in Greedy Best-First Search (GBFS) for satisficing planning. First, we show the potential of exploration-based techniques by combining GBFS and random walk exploration locally. We then conduct deep analysis on how flaws in heuristics impact GBFS’s performance. Uninformative Heuristic Regions (UHRs) and Early Mistakes (EMs) for GBFS are analyzed and illustrated on a number of International Planning Competition (IPC) benchmarks. Corresponding solutions, namely Greedy Best-First Search with Local Exploration (GBFS-LE) and Type-based Greedy Best-First Search (Type-GBFS), are proposed and shown to outperform GBFS substantially. While this thesis mainly focuses on improving coverage (number of problems solved) with exploration-based techniques, we also introduce the Diverse Anytime Search (DAS) framework, which reduces unproductive time and improves plan quality by randomized exploration. Finally, we integrate these techniques and build the new satisficing planner Jasper, which ranked 4th of 20 planners in the Sequential Satisficing track of IPC-2014 and solved the largest number of problems among non-portfolio-based planners.

We investigate planning for self-interested agents in large multi-agent simulations. We present two heuristic algorithms that exploit different domain-specific properties in order to find high quality plans with reduced amount of computations. We further suggest finding a formal framework for describing these properties and complementing our previous mostly experimental results by formal guarantees.