Plan generation using a method of deductive program synthesis

In this paper we introduce a planning approach based on a method of deductive program synthesis. The program synthesis system we rely upon takes first-order specifications and from these derives recursive programs automatically. It uses a set of transformation rules whose applications are guided by an overall strategy. Additionally several heuristics are involved which considerably reduce the search space. We show by means of an example taken from the blocks world how even recursive plans can be obtained with this method. Some modifications of the synthesis strategy and heuristics are discussed, which are necessary to obtain a powerful and automatic planning system. Finally it is shown how subplans can be introduced and generated separately

A new logical framework for deductive planning

In this paper we present a logical framework for defining consistent axiomatizations of planning domains. A language to define basic actions and structured plans is embedded in a logic. This allows general properties of a whole planning scenario to be proved as well as plans to be formed deductively. In particular, frame assertions and domain constraints as invariants of the basic actions can be formulated and proved. Even for complex plans most frame assertions are obtained by purely syntactic analysis. In such cases the formal proof can be generated in a uniform way. The formalism we introduce is especially useful when treating recursive plans. A tactical theorem prover, the Karlsruhe Interactive Verifier KIV is used to implement this logical framework

Deductive planning and plan reuse in a command language environment

In this paper we introduce a deductive planning system currently being developed as the kernel of an intelligent help system. It consists of a deductive planner and a plan reuse component and with that provides planning from first as well as planning from second principles. Both components rely upon an interval-based temporal logic. The deductive formalisms realizing plan formation from formal specifications and the reuse of already existing plans respectively are presented and demonstrated by examples taken from an operating system\u27s domain

X and more Parallelism. Integrating LTL-Next into SAT-based Planning with Trajectory Constraints while Allowing for even more Parallelism

Linear temporal logic (LTL) provides expressive means to specify temporally extended goals as well as preferences. Recent research has focussed on compilation techniques, i.e., methods to alter the domain ensuring that every solution adheres to the temporally extended goals. This requires either new actions or an construction that is exponential in the size of the formula. A translation into boolean satisfiability (SAT) on the other hand requires neither. So far only one such encoding exists, which is based on the parallel $\exists$-step encoding for classical planning. We show a connection between it and recently developed compilation techniques for LTL, which may be exploited in the future. The major drawback of the encoding is that it is limited to LTL without the X operator. We show how to integrate X and describe two new encodings, which allow for more parallelism than the original encoding. An empirical evaluation shows that the new encodings outperform the current state-of-the-art encoding

PHI : a logic-based tool for intelligent help systems

We introduce a system which improves the performance of intelligent help systems by supplying them with plan generation and plan recognition components. Both components work in close mutual cooperation. We demonstrate two modes of cross-talk between them, one where plan recognition is done on the basis of abstract plans provided by the planner and the other where optimal plans are generated based on recognition results. The examples which are presented are taken from an operating system domain, namely from the UNIX mail domain. Our system is completely logic-based. Relying on a common logical framework--the interval-based modal temporal logic LLP which we have developed--both components are implemented as special purpose inference procedures. Plan generation from first and second principles is provided and carried out deductively, whereas plan recognition follows a new abductive approach for modal logics. The plan recognizer is additionally supplied with a probabilistic reasoner as a means to adjust the help provided for user-specific characteristics

Integrated plan generation and recognition : a logic-based approach

The work we present in this paper is settled within the field of intelligent help systems. Intelligent help systems aim at supporting users of application systems by the achievements of qualified experts. In order to provide such qualified support our approach is based on the integration of plan generation and plan recognition components. Plan recognition in this context serves to identify the users goals and so forms the basis for an active user support. The planning component dynamically generates plans which are proposed for the user to reach her goal. We introduce a logic-based approach where plan generation and plan recognition is done on a common logical basis and both components work in some kind of cross-talk

Technological roadmap on AI planning and scheduling

At the beginning of the new century, Information Technologies had become basic and indispensable constituents of the production and preparation processes for all kinds of goods and services and with that are largely influencing both the working and private life of nearly every citizen. This development will continue and even further grow with the continually increasing use of the Internet in production, business, science, education, and everyday societal and private undertaking. Recent years have shown, however, that a dramatic enhancement of software capabilities is required, when aiming to continuously provide advanced and competitive products and services in all these fast developing sectors. It includes the development of intelligent systems – systems that are more autonomous, flexible, and robust than today’s conventional software. Intelligent Planning and Scheduling is a key enabling technology for intelligent systems. It has been developed and matured over the last three decades and has successfully been employed for a variety of applications in commerce, industry, education, medicine, public transport, defense, and government. This document reviews the state-of-the-art in key application and technical areas of Intelligent Planning and Scheduling. It identifies the most important research, development, and technology transfer efforts required in the coming 3 to 10 years and shows the way forward to meet these challenges in the short-, medium- and longer-term future. The roadmap has been developed under the regime of PLANET – the European Network of Excellence in AI Planning. This network, established by the European Commission in 1998, is the co-ordinating framework for research, development, and technology transfer in the field of Intelligent Planning and Scheduling in Europe. A large number of people have contributed to this document including the members of PLANET non- European international experts, and a number of independent expert peer reviewers. All of them are acknowledged in a separate section of this document. Intelligent Planning and Scheduling is a far-reaching technology. Accepting the challenges and progressing along the directions pointed out in this roadmap will enable a new generation of intelligent application systems in a wide variety of industrial, commercial, public, and private sectors

Conditioned Belief Propagation Revisited (Extended Version) Conditioned Belief Propagation Revisited (Extended Version)

Belief Propagation (BP) applied to cyclic problems is a well known approximate inference scheme for probabilistic graphical models. To improve its accuracy, Conditioned Belief Propagation (CBP) has been proposed, which splits a problem into subproblems by conditioning on variables, applies BP to subproblems, and merges the results to produce an answer to the original problem. In this work, we propose a reformulated version of CBP that exhibits anytime behavior and allows for more specific tuning by formalizing a further aspect of the algorithm through the use of a leaf selection heuristic. We propose several simple and easy to compute heuristics and demonstrate their performance using an empirical evaluation on randomly generated problems