This paper provides theoretical and empirical comparisons of three recent hierarchical plan repair algorithms: SHOPFIXER, IPYHOPPER, and REWRITE. Our theoretical results show that the three algorithms correspond to three different definitions of the plan repair problem, leading to differences in the algorithms’ search spaces, the repair problems they can solve, and the kinds of repairs they can make. Understanding these distinctions is important when choosing a repair method for any given application. Building on the theoretical results, we evaluate the algorithms empirically in a series of benchmark planning problems. Our empirical results provide more detailed insight into the run- time repair performance of these systems and the coverage of the repair problems solved, based on algorithmic properties such as replanning, chronological backtracking, and back- jumping over plan trees.

Understanding the implicit values and beliefs of diverse groups and cultures using qualitative texts – such as long-form narratives – and domain-expert interviews is a fundamental goal of social anthropology. This paper builds upon a 2022 study that introduced the NLP task of Recognizing Value Resonance (RVR) for gauging perspective – positive, negative, or neutral – on implicit values and beliefs in textual pairs. This study included a novel hand-annotated dataset, the World Values Corpus (WVC), designed to simulate the task of RVR, and a transformer-based model, Resonance-Tuned RoBERTa, designed to model the task. We extend existing work by refining the task definition and releasing the World Values Corpus (WVC) dataset. We further conduct several validation experiments designed to robustly evaluate the need for task specific modeling, even in the world of LLMs. Finally, we present two additional Resonance-Tuned models trained over extended RVR datasets, designed to improve RVR model versatility and robustness. Our results demonstrate that the Resonance-Tuned models outperform top-performing Recognizing Textual Entailment (RTE) models in recognizing value resonance as well as zero-shot GPT-3.5 under several different prompt structures, emphasizing its practical applicability. Our findings highlight the potential of RVR in capturing cultural values within texts and the importance of task-specific modeling.

Laboratory protocols are critical to biological research and development, yet difficult to communicate and reproduce across projects, investigators, and organizations. While many attempts have been made to address this challenge, there is currently no available protocol representation that is unambiguous enough for precise interpretation and automation, yet simultaneously “human friendly” and abstract enough to enable reuse and adaptation. The Laboratory Open Protocol language (LabOP) is a free and open protocol representation aiming to address this gap, building on a foundation of UML, Autoprotocol, Aquarium, SBOL RDF, and the Provenance Ontology. LabOP provides a linked-data representation both for protocols and for records of their execution and the resulting data, as well as a framework for exporting from LabOP for execution by either humans or laboratory automation. LabOP is currently implemented in the form of an RDF knowledge representation, specification document, and Python library, and supports execution as manual “paper protocols,” by Autoprotocol or by Opentrons. From this initial implementation, LabOP is being further developed as an open community effort.

PDDL+ planning involves reasoning about mixed discrete-continuous change over time. Nearly all PDDL+ planners assume that continuous change is linear. We present a new technique that accommodates nonlinear change by encoding problems as nonlinear hybrid systems. Using this encoding, we apply a Satisfiability Modulo Theories (SMT) solver to find plans. We show that it is important to use novel planning- specific heuristics for variable and value selection for SMT solving, which is inspired by recent advances in planning as SAT. We show the promising performance of the resulting solver on challenging nonlinear problems.

In this paper we present a plan-plan distance metric based on Kolmogorov(Algorithmic) complexity. Generating diverse sets of plans is useful for task ssuch as probing user preferences and reasoning about vulnerability to cyberattacks. Generating diverse plans, and comparing different diverse planning approaches requires a domain-independent, theoretically motivated definition of the diversity distance between plans. Previously proposed diversity measures are not theoretically motivated, and can provide inconsistent results on the sameplans. We define the diversity of plans in terms of how surprising one plan is givenanother or, its inverse, the conditional information in one plan givenanother. Kolmogorov complexity provides a domain independent theory of conditional information. While Kolmogorov complexity is not computable, a related metric, Normalized Compression Distance (NCD), provides a well-behaved approximation. In this paper we introduce NCD as an alternative diversity metric, and analyze its performance empirically, in comparison with previous diversity measures, showing strengths and weaknesses of each.We also examine the use of different compressor sin NCD. We show how NCD can be used to select a training set for HTN learning,giving an example of the utility of diversity metrics. We conclude withsuggestions for future work on improving, extending, and applying it to serve new applications.

This paper extends existing plan recognition research to handle plans containing loops. We supply an encoding of plans with loops for recognition, based on techniques used to parse lexicalized grammars, and demonstrate its effectiveness empirically. To do this, the paper first shows how encoding plan libraries as context free grammars permits the application of standard rewriting techniques to remove left recursion and ε-productions, thereby enabling polynomial time parsing. However, these techniques alone fail to provide efficient algorithms for plan recognition. We show how the loop-handling methods from formal grammars can be extended to the more general plan recognition problem and provide a method for encoding loops in an existing plan recognition system that scales linearly in the number of loop iterations.

We describe a novel architecture for network defense designed for scaling to very high data rates (100 Gb/s) and very large user populations. Scaling requires both efficient attack detection algorithms as well as appropriate an execution environment. Our architecture considers the time budget of traffic data extraction and algorithmic processing, provides a suite of detection algorithms - each designed to present different and complementary views of the data-that generate many ¿traffic events,¿ and reduces false positives by correlating these traffic events into benign or malicious hypotheses.

We are developing autonomous control systems for mission-critical domains that require hard real-time performance guarantees. To automatically build reactive plans that meet these requirements, we use formal verification (model checking) techniques to assess the quality of plans as they are built. The verification process uses precise timed automaton models of the executive that will run the resulting reactive plan. This reflexive modeling allows our system to formally verify not just that its plans are correct, but that they will be executed correctly.

Concerns the development of autonomous, flexible control systems for mission-critical applications such as unmanned aerial vehicles (UAV) and deep space probes. These applications require hybrid real-time control systems, capable of effectively managing both discrete and continuous controllable parameters to maintain system safety and achieve system goals. Using the CIRCA architecture and its state space planner (SSP) for adaptive real-time control systems, these controllers are synthesized automatically and dynamically, online, while the platform is operating. Unlike many other AI planning systems, CIRCA's automatically-generated control plans have strong temporal semantics and provide safety guarantees, ensuring that the controlled system will avoid all forms of mission-critical failure. This paper is intended to convey an intuitive, qualitative understanding of the way CIRCA verifies its plans using model checking techniques.

To be truly useful, mobile robots need to be fairly autonomous and easy to control. This is especially true in situations where multiple robots are used, due to the increase in sensory information and the fact that the robots can interfere with one another. The paper describes a system that integrates autonomous navigation, a task executive, task planning, and an intuitive graphical user interface to control multiple, heterogeneous robots. We have demonstrated a prototype system that plans and coordinates the deployment of teams of robots. Testing has shown the effectiveness and robustness of the system, and of the coordination strategies in particular.

In this project, w e dev eloped a technique for extracting useful information from databases that contain both xedformat and free-text elds.The present state of the art in data mining is a schism betw een tec hniques that handle only xed-format data (pattern recognition, classi cation algorithms from machine learning), and techniques designed for free-form text (information retrieval).Advanced knowledge disco very tec hnologies ha ve been developed in both research areas, but systems that can categorize or cluster records containing both kinds of data are still lacking.Speci cally, w e examined database records from a Honeywell service cen ter to extract information about the expected cost of dierent kinds of service requests.Our goal was to test the h ypothesis that incorporating information from free-text elds would provide a better categorization of these records; in this case, better predictions of the cost of the service call.In our w ork, we h a ve i n tegrated feature extraction and clustering techniques from information retrieval with classi cation algorithms from machine learning in order to categorize the hybrid elds.Our preliminary results suggested that incorporating free-form text could potentially induce better classi cation models.

No abstract available.

Selman and Kautz's paper on the logic of model-preference defaults introduced a new language and tractability results. This paper assesses the contributions of that work, including the properties of the logic itself, its relationship to some previous

A method for incrementally constructing belief networks, which are directed acyclic graph representations for probability distributions, is described. A network-construction language, FRAIL3, which is similar to a forward-chaining language using data dependencies but has additional features for specifying distributions, was developed. A particularly important feature of this language is that is allows the user to conveniently specify conditional probability matrices using stereotyped models of intercausal interaction. Using FRAIL3, one can define parmeterized classes of probabilistic models. These parameterized models make it possible to apply probabilistic reasoning to problems for which it is impractical to have a single large, static model.>

We propose that logic (enhanced to encode probability information) is a good way of characterizing semantic interpretation. In support of this we give a fragment of an axiomatization for word-sense disambiguation, nounphrase (and verb) reference, and case disambiguation. We describe an inference engine (Frail3) which actually takes this axiomatization and uses it to drive the semantic interpretation process. We claim three benefits from this scheme. First, the interface between semantic interpretation and pragmatics has always been problematic, since all of the above tasks in general require pragmatic inference. Now the interface is trival, since both semantic interpretation and pragmatics use the same vocabulary and inference engine. The second benefit, related to the first, is that semantic guidance of syntax is a side effect of the interpretation. The third benefit is the elegance of the semantic interpretation theory. A few simple rules capture a remarkable diversity of semantic phenomena.