Error Analysis and Correction for Weighted A*'s Suboptimality (Extended Version)

Weighted A* (wA*) is a widely used algorithm for rapidly, but suboptimally, solving planning and search problems. The cost of the solution it produces is guaranteed to be at most W times the optimal solution cost, where W is the weight wA* uses in prioritizing open nodes. W is therefore a suboptimality bound for the solution produced by wA*. There is broad consensus that this bound is not very accurate, that the actual suboptimality of wA*'s solution is often much less than W times optimal. However, there is very little published evidence supporting that view, and no existing explanation of why W is a poor bound. This paper fills in these gaps in the literature. We begin with a large-scale experiment demonstrating that, across a wide variety of domains and heuristics for those domains, W is indeed very often far from the true suboptimality of wA*'s solution. We then analytically identify the potential sources of error. Finally, we present a practical method for correcting for two of these sources of error and experimentally show that the correction frequently eliminates much of the error.Comment: Published as a short paper in the 12th Annual Symposium on Combinatorial Search, SoCS 201

A Combinatorial Bit Bang Leading to Quaternions

This paper describes in detail how (discrete) quaternions - ie. the abstract structure of 3-D space - emerge from, first, the Void, and thence from primitive combinatorial structures, using only the exclusion and co-occurrence of otherwise unspecified events. We show how this computational view supplements and provides an interpretation for the mathematical structures, and derive quark structure. The build-up is emergently hierarchical, compatible with both quantum mechanics and relativity, and can be extended upwards to the macroscopic. The mathematics is that of Clifford algebras emplaced in the homology-cohomology structure pioneered by Kron. Interestingly, the ideas presented here were originally developed by the author to resolve fundamental limitations of existing AI paradigms. As such, the approach can be used for learning, planning, vision, NLP, pattern recognition; and as well, for modelling, simulation, and implementation of complex systems, eg. biological.Comment: 23 pages, 4 figure

Distributed debugging and tumult

A description is given of Tumult (Twente university multicomputer) and its operating system, along with considerations about parallel debugging, examples of parallel debuggers, and the proposed debugger for Tumult. Problems related to debugging distributed systems and solutions found in other distributed debuggers are discussed. The following are the main features of the debugger: it is event based, using a monitor for intercepting these events; record and reply are the main debugging techniques; preprocessing of events is done by programmable filters; the user interface is graphical, using grouping as the main abstraction mechanism. Parts of the debugger, as well as initial versions of the global and local event managers, have been implemented. A slow serial link between the front-end processor and the Tumult system has been replaced by a fast SCSI communication link. The user interface is partly textual, partly graphical. The languages used to implement the debugger are Modula-2 and C. The X Window System and OSF/Motif are used for the graphical user interfac

On debugging in a parallel system

In this paper a description is given of a partly implemented parallel debugger for the Twente University Multicomputer (TUMULT). The system's basic method for exchange of data is message passing. Experience has learned that most programming errors in application software are made in calls to the kernel and the interprocess communication. The debugger is intended to be used for locating bugs at this level in the application software. It is assumed that basic blocks of the debuggee can be debugged using a traditional sequential sourcelevel debugger

Faster optimal and suboptimal hierarchical search

In problem domains for which an informed admissible heuristic function is not available, one attractive approach is hierarchical search. Hierarchical search uses search in an abstracted version of the problem to dynamically generate heuristic values. This thesis makes three contributions to hierarchical search. First, we propose a simple modification to the state-of-the-art algorithm Switchback that reduces the number of expansions (and hence the running time) by approximately half, while maintaining its guarantee of optimality. Second, we propose a new algorithm for suboptimal hierarchical search, called Switch. Empirical results suggest that Switch yields faster search than straightforward modifications of Switchback, such as weighting the heuristic. Finally, we propose a modification to our optimal algorithm that uses multiple additive abstractions in order to improve performance of both optimal and suboptimal hierarchical search on some domains

Planning under time pressure

Heuristic search is a technique used pervasively in artificial intelligence and automated planning. Often an agent is given a task that it would like to solve as quickly as possible. It must allocate its time between planning the actions to achieve the task and actually executing them. We call this problem planning under time pressure. Most popular heuristic search algorithms are ill-suited for this setting, as they either search a lot to find short plans or search a little and find long plans. The thesis of this dissertation is: when under time pressure, an automated agent should explicitly attempt to minimize the sum of planning and execution times, not just one or just the other. This dissertation makes four contributions. First we present new algorithms that use modern multi-core CPUs to decrease planning time without increasing execution. Second, we introduce a new model for predicting the performance of iterative-deepening search. The model is as accurate as previous offline techniques when using less training data, but can also be used online to reduce the overhead of iterative-deepening search, resulting in faster planning. Third we show offline planning algorithms that directly attempt to minimize the sum of planning and execution times. And, fourth we consider algorithms that plan online in parallel with execution. Both offline and online algorithms account for a user-specified preference between search and execution, and can greatly outperform the standard utility-oblivious techniques. By addressing the problem of planning under time pressure, these contributions demonstrate that heuristic search is no longer restricted to optimizing solution cost, obviating the need to choose between slow search times and expensive solutions

Context-Independent Task Knowledge for Neurosymbolic Reasoning in Cognitive Robotics

One of the current main goals of artificial intelligence and robotics research is the creation of an artificial assistant which can have flexible, human like behavior, in order to accomplish everyday tasks. A lot of what is context-independent task knowledge to the human is what enables this flexibility at multiple levels of cognition. In this scope the author analyzes how to acquire, represent and disambiguate symbolic knowledge representing context-independent task knowledge, abstracted from multiple instances: this thesis elaborates the incurred problems, implementation constraints, current state-of-the-art practices and ultimately the solutions newly introduced in this scope. The author specifically discusses acquisition of context-independent task knowledge from large amounts of human-written texts and their reusability in the robotics domain; the acquisition of knowledge on human musculoskeletal dependencies constraining motion which allows a better higher level representation of observed trajectories; the means of verbalization of partial contextual and instruction knowledge, increasing interaction possibilities with the human as well as contextual adaptation. All the aforementioned points are supported by evaluation in heterogeneous setups, to bring a view on how to make optimal use of statistical & symbolic applications (i.e. neurosymbolic reasoning) in cognitive robotics. This work has been performed to enable context-adaptable artificial assistants, by bringing together knowledge on what is usually regarded as context-independent task knowledge

Envisioning the qualitative effects of robot manipulation actions using simulation-based projections

Autonomous robots that are to perform complex everyday tasks such as making pancakes have to understand how the effects of an action depend on the way the action is executed. Within Artificial Intelligence, classical planning reasons about whether actions are executable, but makes the assumption that the actions will succeed (with some probability). In this work, we have designed, implemented, and analyzed a framework that allows us to envision the physical effects of robot manipulation actions. We consider envisioning to be a qualitative reasoning method that reasons about actions and their effects based on simulation-based projections. Thereby it allows a robot to infer what could happen when it performs a task in a certain way. This is achieved by translating a qualitative physics problem into a parameterized simulation problem; performing a detailed physics-based simulation of a robot plan; logging the state evolution into appropriate data structures; and then translating these sub-symbolic data structures into interval-based first-order symbolic, qualitative representations, called timelines. The result of the envisioning is a set of detailed narratives represented by timelines which are then used to infer answers to qualitative reasoning problems. By envisioning the outcome of actions before committing to them, a robot is able to reason about physical phenomena and can therefore prevent itself from ending up in unwanted situations. Using this approach, robots can perform manipulation tasks more efficiently, robustly, and flexibly, and they can even successfully accomplish previously unknown variations of tasks

Metareasoning for Heuristic Search Using Uncertainty

Heuristic search methods are widely used in many real-world autonomous systems. Yet, people always want to solve search problems that are larger than time allows. To address these challenging problems, even suboptimally, a planning agent should be smart enough to intelligently allocate its computational resources, to think carefully about where in the state space it should spend time searching. For finding optimal solutions, we must examine every node that is not provably too expensive. In contrast, to find suboptimal solutions when under time pressure, we need to be very selective about which nodes to examine. In this dissertation, we will demonstrate that estimates of uncertainty, represented as belief distributions, can be used to drive search effectively. This type of algorithmic approach is known as metareasoning, which refers to reasoning about which reasoning to do. We will provide examples of improved algorithms for real-time search, bounded-cost search, and situated planning