Dynamic Controllability of Temporally-flexible Reactive Programs

In this paper we extend dynamic controllability of temporally-flexible plans to temporally-flexible reactive programs. We consider three reactive programming language constructs whose behavior depends on runtime observations; conditional execution, iteration, and exception handling. Temporally-flexible reactive programs are distinguished from temporally-flexible plans in that program execution is conditioned on the runtime state of the world. In addition, exceptions are thrown and caught at runtime in response to violated timing constraints, and handled exceptions are considered successful program executions. Dynamic controllability corresponds to a guarantee that a program will execute to completion, despite runtime constraint violations and uncertainty in runtime state. An algorithm is developed which frames the dynamic controllability problem as an AND/OR search tree over possible program executions. A key advantage of this approach is the ability to enumerate only a subset of possible program executions that guarantees dynamic controllability, framed as an AND/OR solution subtree

Deciding the Consistency of Branching Time Interval Networks

Allen’s Interval Algebra (IA) is one of the most prominent formalisms in the area of qualitative temporal reasoning; however, its applications are naturally restricted to linear flows of time. When dealing with nonlinear time, Allen’s algebra can be extended in several ways, and, as suggested by Ragni and Wölfl, a possible solution consists in defining the Branching Algebra (BA) as a set of 19 basic relations (13 basic linear relations plus 6 new basic nonlinear ones) in such a way that each basic relation between two intervals is completely defined by the relative position of the endpoints on a tree-like partial order. While the problem of deciding the consistency of a network of IA-constraints is well-studied, and every subset of the IA has been classified with respect to the tractability of its consistency problem, the fragments of the BA have received less attention. In this paper, we first define the notion of convex BA-relation, and, then, we prove that the consistency of a network of convex BA-relations can be decided via path consistency, and is therefore a polynomial problem. This is the first non-trivial tractable fragment of the BA; given the clear parallel with the linear case, our contribution poses the bases for a deeper study of fragments of BA towards their complete classification

Hybrid SAT-Based Consistency Checking Algorithms for Simple Temporal Networks with Decisions

A Simple Temporal Network (STN) consists of time points modeling temporal events and constraints modeling the minimal and maximal temporal distance between them. A Simple Temporal Network with Decisions (STND) extends an STN by adding decision time points to model temporal plans with decisions. A decision time point is a special kind of time point that once executed allows for deciding a truth value for an associated Boolean proposition. Furthermore, STNDs label time points and constraints by conjunctions of literals saying for which scenarios (i.e., complete truth value assignments to the propositions) they are relevant. Thus, an STND models a family of STNs each obtained as a projection of the initial STND onto a scenario. An STND is consistent if there exists a consistent scenario (i.e., a scenario such that the corresponding STN projection is consistent). Recently, a hybrid SAT-based consistency checking algorithm (HSCC) was proposed to check the consistency of an STND. Unfortunately, that approach lacks experimental evaluation and does not allow for the synthesis of all consistent scenarios. In this paper, we propose an incremental HSCC algorithm for STNDs that (i) is faster than the previous one and (ii) allows for the synthesis of all consistent scenarios and related early execution schedules (offline temporal planning). Then, we carry out an experimental evaluation with KAPPA, a tool that we developed for STNDs. Finally, we prove that STNDs and disjunctive temporal networks (DTNs) are equivalent

The SAT-based Approach to Separation Logic

The SAT-based approach to the decision problem for expressive, decidable, quantifier-free first-order theories has been investigated with remarkable results at least since 1993. One such theory, successfully employed in the formal verification of complex, infinite state systems, is Separation Logic (SL), which combines Boolean logic with arithmetic constraints of the form x − y ⋈ c, where ⋈ is ≤, , ≥, =, or ≠. The SAT-based approach to SL was first proposed and implemented in 1999: the results in terms of performance were good, and since then a number of other systems for SL have appeared. In this paper we focus on the problem of building efficient SAT-based decision procedures for SL. We present the basic procedure and four optimizations that improve dramatically its effectiveness in most cases: (a) IS 2 preprocessing, (b) early pruning, (c) model reduction, and (d) best reason detection. For each technique we give an example of how it might improve the performance. Furthermore, for the first three techniques, we give a pseudo-code representation and formally state the soundness and completeness of the resulting optimized procedure. We also show how it is possible to check the satisfiability of valuations involving constraints of the form x − y < c using the Bellman-Ford algorithm. Lastly, we present an extensive comparative experimental analysis, showing that our solver TSAT++, built along the lines described in this paper, is currently the state of the art on various classes of problems, including randomly generated, hand-made, and real-world instance

Modelling and solving temporal reasoning as propositional satisfiability

AbstractRepresenting and reasoning about time dependent information is a key research issue in many areas of computer science and artificial intelligence. One of the best known and widely used formalisms for representing interval-based qualitative temporal information is Allen's interval algebra (IA). The fundamental reasoning task in IA is to find a scenario that is consistent with the given information. This problem is in general NP-complete.In this paper, we investigate how an interval-based representation, or IA network, can be encoded into a propositional formula of Boolean variables and/or predicates in decidable theories. Our task is to discover whether satisfying such a formula can be more efficient than finding a consistent scenario for the original problem. There are two basic approaches to modelling an IA network: one represents the relations between intervals as variables and the other represents the end-points of each interval as variables. By combining these two approaches with three different Boolean satisfiability (SAT) encoding schemes, we produced six encoding schemes for converting IA to SAT. In addition, we also showed how IA networks can be formulated into satisfiability modulo theories (SMT) formulae based on the quantifier-free integer difference logic (QF-IDL). These encodings were empirically studied using randomly generated IA problems of sizes ranging from 20 to 100 nodes. A general conclusion we draw from these experimental results is that encoding IA into SAT produces better results than existing approaches. More specifically, we show that the new point-based 1-D support SAT encoding of IA produces consistently better results than the other alternatives considered. In comparison with the six different SAT encodings, the SMT encoding came fourth after the point-based and interval-based 1-D support schemes and the point-based direct scheme. Further, we observe that the phase transition region maps directly from the IA encoding to each SAT or SMT encoding, but, surprisingly, the location of the hard region varies according to the encoding scheme. Our results also show a fixed performance ranking order over the various encoding schemes

Knowledge-Based Schematics Drafting: Aesthetic Configuration as a Design Task

Depicting an electrical circuit by a schematic is a tedious task that is a good candidate for automation. Programs that draft schematics with the usual algorithmic approach do not fully exploit knowledge of circuit function, relying mainly on the circuit topology. The extra-topological circuit characteristics are what an engineer uses to understand a schematic; human drafters take these characteristics into account when drawing a schematic. This document presents a knowledge base and an architecture for drafting arithmetic digital circuits having a single theme. The relevance and limitations of this architecture and knowledge base for other types of circuit are explored. It is argued that the task of schematics drafting is one of aesthetic design. The affect of aesthetic criteria on the program architecture is discussed. The circuit layout constraint language, the program's search regimen, and the backtracking scheme are highlighted and explained in detail.MIT Artificial Intelligence Laborator

The role of artificial intelligence techniques in scheduling systems

Artificial Intelligence (AI) techniques provide good solutions for many of the problems which are characteristic of scheduling applications. However, scheduling is a large, complex heterogeneous problem. Different applications will require different solutions. Any individual application will require the use of a variety of techniques, including both AI and conventional software methods. The operational context of the scheduling system will also play a large role in design considerations. The key is to identify those places where a specific AI technique is in fact the preferable solution, and to integrate that technique into the overall architecture