Nondeterministic Actions Research Articles

Weak and Strong Reversibility of Non-deterministic Actions: Universality and Uniformity

Weak and Strong Reversibility of Non-deterministic Actions: Universality and Uniformity

Ensure: Towards Reliable Control of Cyber-Physical Systems Under Uncertainty

Cyber-physical systems (CPSs) are complex ensembles of physical and cyber components that cooperate to offer dynamic and adaptive functionalities. Uncertainty can arise from a plethora of sources in the entangled components, ranging from the unreliable perception, the nondeterministic action effects, to even the changes in the environment. Existing controlling approaches, such as those using Markov decision process, have limited ability in handling uncertainty. To address the challenge, in this article, we novelly propose using partially observable Markov decision processes (POMDPs) to model CPS under uncertainty and show that common types of uncertainties can be modeled by partial observations and nondeterministic actions over probabilistic distributions. With POMDPs, strategies that can optimally control CPS are synthesized. We further propose a strategywise verification method, which resolves the difficult problem of verifying the entire POMDP, to offer reliable controlling strategies. Experiments on two representative cases of CPS show promising results compared with existing approaches.

Abstract A025: Agency of the Cell: Convergence between Origin of Life, Evolution, Cognition and Cancer

Abstract All cells exhibit agency and evolve purposefully. This is why cancer has outwitted doctors for 100 years and why Stage 3-4 patients are no better in 2022 than in 1930. Evolution was presumed to be random and purposeless, yet all cells possess cognition (Shapiro 2020). Cell behavior is normally algorithmic, but uniquely responds to novel situations. This is what makes evolution (and cancer) possible. Cognition—sensing and responding to the environment—is the unifying principle behind the genetic code, origin of life, evolution, consciousness, artificial intelligence, and cancer. Thus the central question in biology is: What is the nature and origin of cognition? A solution to the origin of the genetic code, for which the presenter’s organization offers a $10 million prize (announced at the Royal Society in 2019) is an unsolved cognition problem. This choice is a non-deterministic action of a free agent with sensory capacity and memory. It is not computable from prior states. As well as reading and reacting to its environment, it anticipates future threats, chooses goals and reasons inductively. Computers do none of these things. This new model explains the futility of reducing cancer to a single random or algorithmic component, and suggests we should treat cancer as an autonomous purposeful agent with unique identity and capacity to choose. Citation Format: Perry S. Marshall. Agency of the Cell: Convergence between Origin of Life, Evolution, Cognition and Cancer [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr A025.

Nonlinear Dynamic Structural Analysis of Buildings Considering the Wind Nondeterministic Actions

AbstractIn recent decades, the technological advances in civil construction have driven projects and construction of tall buildings in several countries. However, increasing the slenderness of the structural design has been crucial for reducing the natural frequencies values, generating, in some situations, excessive vibrations and human discomfort. This way, an aspect generally disregarded in day‐to‐day project practice is related to the geometric nonlinearity effect on the buildings structural response. Therefore, this work aims to assess the dynamic structural behaviour of a 48 floors and 172.8 m height steel‐concrete composite building, when subjected to nondeterministic wind actions. The results have indicated relevant differences when the geometric nonlinearity effect was included in the building dynamic analysis.

Knowledge Compilation for Nondeterministic Action Languages

We study different languages for representing nondeterministic actions in planning from the point of view of knowledge compilation. Precisely, we consider succintness issues (how succinct is the description of an action in each language?) and complexity issues (tractability or hardness of several queries which arise naturally in planning and belief tracking). We study an abstract, nondeterministic version of PDDL, nondeterministic conditional STRIPS, the language NNFAT of NNF action theories, and the language NPDDLseq obtained by adding a sequence operator to nondeterministic PDDL. We show that these languages have different succinctness and different complexity even for the most natural queries.

Fully Observable Nondeterministic HTN Planning -- Formalisation and Complexity Results

Much progress has been made in advancing the state of the art of HTN planning theory in recent years. However, scarce studies have been made with regards to the theory and complexity of HTN problems on nondeterministic domains. In this paper we provide a novel formalisation for fully observable nondeterministic HTN planning. We propose and study different solution criteria which differ in when nondeterministic action outcomes are considered: at plan generation or at plan execution. We integrate our solution criteria with notions of weak and strong plans canonical in nondeterministic planning and identify similarities and differences with plans in other fields of AI planning. We also provide completeness results for a majority of HTN problem subclasses and show the significant result that problems are not made any harder under nondeterminism for certain solution criteria by using compilation techniques to deterministic HTN planning. This supports and justifies the practicality and scalability of extending HTN problems over nondeterministic domains to deal with real world scenarios.

Model independent approach to probabilistic models

There is a lot of research on probabilistic transition systems. There are not many studies in probabilistic process models. The lack of investigation into the interactive aspect of probabilistic processes is mainly due to the difficulty caused by the discrepancy between probabilistic choices and nondeterministic actions. The paper proposes a uniform approach to probabilistic process models and a bisimulation theory for probabilistic concurrency.

A machine learning approach for optimizing heuristic decision‐making in Web Ontology Language reasoners

AbstractDescription logics (DLs) are formalisms for representing knowledge bases of application domains. The Web Ontology Language (OWL) is a syntactic variant of a very expressive DL. OWL reasoners can infer implied information from OWL ontologies. The performance of OWL reasoners can be severely affected by situations that require decision‐making over many alternatives. Such a nondeterministic behavior is often controlled by heuristics that are based on insufficient information. This article proposes a novel OWL reasoning approach that applies machine learning (ML) to implement pragmatic and optimal decision‐making strategies in such situations. Disjunctions occurring in ontologies are one source of nondeterministic actions in reasoners. We propose two ML‐based approaches to reduce the nondeterminism caused by dealing with disjunctions. The first approach is restricted to propositional DL while the second one can deal with standard DL. Both approaches speed up our ML‐based reasoner by up to two orders of magnitude in comparison to the non‐ML reasoner. Another source of nondeterministic actions is the order in which tableau rules should be applied. On average, our ML‐based approach achieves a speedup of two orders of magnitude when compared to the most expensive rule ordering of the non‐ML reasoner.

Compact Policies for Fully Observable Non-Deterministic Planning as SAT

Fully observable non-deterministic (FOND) planning is becoming increasingly important as an approach for computing proper policies in probabilistic planning, extended temporal plans in LTL planning, and general plans in generalized planning. In this work, we introduce a SAT encoding for FOND planning that is compact and can produce compact strong cyclic policies. Simple variations of the encodings are also introduced for strong planning and for what we call, dual FOND planning, where some non-deterministic actions are assumed to be fair (e.g., probabilistic) and others unfair (e.g., adversarial). The resulting FOND planners are compared empirically with existing planners over existing and new benchmarks. The notion of ``probabilistic interesting problems'' is also revisited to yield a more comprehensive picture of the strengths and limitations of current FOND planners and the proposed SAT approach.

An Epistemic Approach to Nondeterminism: Believing in the Simplest Course of Events

This paper describes an approach for reasoning in a dynamic domain with nondeterministic actions in which an agent’s (categorical) beliefs correspond to the simplest, or most plausible, course of events consistent with the agent’s observations and beliefs. The account is based on an epistemic extension of the situation calculus, a first-order theory of reasoning about action that accommodates sensing actions. In particular, the account is based on a qualitative theory of nondeterminism. Our position is that for commonsense reasoning, the world is most usefully regarded as deterministic, and that nondeterminism is an epistemic phenomenon, arising from an agent’s limited awareness and perception. The account offers several advantages: an agent has a set of categorical (as opposed to probabilistic) beliefs, yet can deal with equally-likely outcomes (such as in flipping a fair coin) or with outcomes of differing plausibility (such as an action that on rare occasions may fail). The agent maintains as its set of contingent beliefs the most plausible, or simplest, picture of the world, consistent with its beliefs and actions it believes it executed; yet it may modify these in light of later information.

Online belief tracking using regression for contingent planning

In online contingent planning under partial observability an agent decides at each time step on the next action to execute, given its initial knowledge of the world, the actions executed so far, and the observation made. Such agents require some representation of their belief state to determine which actions are valid, or whether the goal has been achieved. Efficient maintenance of a belief state is, given its potential exponential size, a key research challenge in this area. In this paper we develop the theory of regression as a useful tool for belief-state maintenance. We provide a formal description of regression, discussing various alternatives and optimization techniques, and analyze its space and time complexity. In particular, we show that, with some care, the regressed formula will contain variables relevant to the current query only, rather than all variables in the problem description. Consequently, under suitable assumptions, the complexity of regression queries is at most exponential in its contextual width. This parameter is always upper bounded by Bonet and Geffner's width parameter, introduced in their state-of-the-art factored belief tracking (FBT) method. In addition, we show how to obtain a poly-sized circuit representation for the online regression formula even with non-deterministic actions. We provide an empirical comparison of regression with FBT-based belief maintenance, showing the power of regression for online belief tracking. We also suggest caching techniques for regression, and demonstrate their value in reducing runtime in current benchmarks.

Reasoning about actions with loops via Hoare logic

Plans with loops are more general and compact than classical sequential plans, and gaining increasing attentions in artificial intelligence (AI). While many existing approaches mainly focus on algorithmic issues, few work has been devoted to the semantic foundations on planning with loops. In this paper, we first develop a tailored action language AL K, together with two semantics for handling domains with non-deterministic actions and loops. Then we propose a sound and (relative) complete Hoare-style proof system for efficient plan generation and verification under 0-approximation semantics, which uses the so-called idea offline planning and on-line querying strategy in knowledge compilation, i.e., the agent could generate and store short proofs as many as possible in the spare time, and then perform quick query by constructing a long proof from the stored shorter proofs using compositional rule. We argue that both our semantics and proof system could serve as logical foundations for reasoning about actions with loops.

Symbolic Input-Output Conformance Checking for Model-Based Mutation Testing

This paper presents an approach to use symbolic input output conformance checking for mutation-based test case generation. In this approach, a possibly non-deterministic action system model is used as basis for generating a number of mutants. Subsequently after the generation of mutants, the original model and the mutants are simultaneously symbolically executed and tested for conformance. Distinguishing test cases are generated, if non-conformance is detected during this process. Several optimisations of the conformance check are presented and their effectiveness is underpinned by listing experimental results.

Maintenance planning using continuous-state partially observable Markov decision processes and non-linear action models

The signs of deterioration in worldwide infrastructure and the associated socio-economic and environmental losses call for sustainable resource management and policy-making. To this end, this work presents an enhanced variant of partially observable Markov decision processes (POMDPs) for the life cycle assessment and maintenance planning of infrastructure. POMDPs comprise a method, commonly employed in the field of robotics, for decision-making on the basis of uncertain observations. In the work presented herein, a continuous-state POMDP formulation is presented which is adapted to the problem of decision-making for optimal management of civil structures. The aforementioned problem may comprise non-linear and non-deterministic action and observation models. The continuous-state POMDP is herein coupled with a normalised unscented transform (NUT) in order to deliver a framework able to tackle non-linearities that likely characterise action models. The capabilities of this enhanced framework and its applicability to the maintenance planning problem are presented via two applications. In a first illustrative example, the use of the NUT is demonstrated within the framework of the value iteration algorithm. Next, the proposed continuous-state framework is compared against a discrete-state formulation for implementation on a life cycle assessment problem.

CORPP: Commonsense Reasoning and Probabilistic Planning, as Applied to Dialog with a Mobile Robot

In order to be fully robust and responsive to a dynamically changing real-world environment, intelligent robots will need to engage in a variety of simultaneous reasoning modalities. In particular, in this paper we consider their needs to i) reason with commonsense knowledge, ii) model their nondeterministic action outcomes and partial observability, and iii) plan toward maximizing long-term rewards. On one hand, Answer Set Programming (ASP) is good at representing and reasoning with commonsense and default knowledge, but is ill-equipped to plan under probabilistic uncertainty. On the other hand, Partially Observable Markov Decision Processes(POMDPs) are strong at planning under uncertainty toward maximizing long-term rewards, but are not designed to incorporate commonsense knowledge and inference. This paper introduces the CORPP algorithm which combines P-log,a probabilistic extension of ASP, with POMDPs to integrate commonsense reasoning with planning under uncertainty.Our approach is fully implemented and tested on a shopping request identification problem both in simulation and on a real robot. Compared with existing approaches using P-log or POMDPs individually, we observe significant improvements in both efficiency and accuracy.

Cooperative Monitoring to Diagnose Multiagent Plans

Diagnosing the execution of a Multiagent Plan (MAP) means identifying and explaining action failures (i.e., actions that did not reach their expected effects). Current approaches to MAP diagnosis are substantially centralized, and assume that action failures are independent of each other. In this paper, the diagnosis of MAPs, executed in a dynamic and partially observable environment, is addressed in a fully distributed and asynchronous way; in addition, action failures are no longer assumed as independent of each other. The paper presents a novel methodology, named Cooperative Weak-Committed Monitoring (CWCM), enabling agents to cooperate while monitoring their own actions. Cooperation helps the agents to cope with very scarcely observable environments: what an agent cannot observe directly can be acquired from other agents. CWCM exploits nondeterministic action models to carry out two main tasks: detecting action failures and building trajectory-sets (i.e., structures representing the knowledge an agent has about the environment in the recent past). Relying on trajectory-sets, each agent is able to explain its own action failures in terms of exogenous events that have occurred during the execution of the actions themselves. To cope with dependent failures, CWCM is coupled with a diagnostic engine that distinguishes between primary and secondary action failures. An experimental analysis demonstrates that the CWCM methodology, together with the proposed diagnostic inferences, are effective in identifying and explaining action failures even in scenarios where the system observability is significantly reduced.

Symbolic Regression for Non-Deterministic Actions

Symbolic Regression for Non-Deterministic Actions

Turning nondeterminism into parallelism

Nondeterminism is a useful and prevalent concept in the design and implementation of software systems. An important property of nondeterminism is its latent parallelism: A nondeterministic action can evaluate to multiple behaviors. If at least one of these behaviors does not conflict with concurrent tasks, then there is an admissible execution of the action in parallel with these tasks. Unfortunately, existing implementations of the atomic paradigm - optimistic as well as pessimistic - are unable to fully exhaust the parallelism potential of nondeterministic actions, lacking the means to guide concurrent tasks toward nondeterministic choices that minimize interference. This paper investigates the problem of utilizing parallelism due to nondeterminism. We observe that nondeterminism occurs in many real-world codes. We motivate the need for devising coordination mechanisms that can utilize available nondeterminism. We have developed a system featuring such mechanisms, which leverages nondeterminism in a wide class of query operations, allowing a task to look into the future of concurrent tasks that mutate the shared state during query evaluation and reduce conflict accordingly. We evaluate our system on a suite of 12 algorithmic benchmarks of wide applicability, as well as an industrial application. The results are encouraging.

Active Visual Planning for Mobile Robot Teams Using Hierarchical POMDPs

Key challenges to widespread deployment of mobile robots include collaboration and the ability to tailor sensing and information processing to the task at hand. Partially observable Markov decision processes (POMDPs), which are an instance of probabilistic sequential decision-making, can be used to address these challenges in domains characterized by partial observability and nondeterministic action outcomes. However, such formulations tend to be computationally intractable for domains that have large complex state spaces and require robots to respond to dynamic changes. This paper presents a hierarchical decomposition of POMDPs that incorporates adaptive observation functions, constrained convolutional policies, and automatic belief propagation, enabling robots to retain capabilities for different tasks, direct sensing to relevant locations, and determine the sequence of sensing and processing algorithms best suited to any given task. A communication layer is added to the POMDP hierarchy for belief sharing and collaboration in a team of robots. All algorithms are evaluated in simulation and on physical robots, localizing target objects in dynamic indoor domains.

Qualitative Numeric Planning

We consider a new class of planning problems involving a set of non-negative real variables, and a set of non-deterministic actions that increase or decrease the values of these variables by some arbitrary amount. The formulas specifying the initial state, goal state, or action preconditions can only assert whether certain variables are equal to zero or not. Assuming that the state of the variables is fully observable, we obtain two results. First, the solution to the problem can be expressed as a policy mapping qualitative states into actions, where a qualitative state includes a Boolean variable for each original variable, indicating whether its value is zero or not. Second, testing whether any such policy, that may express nested loops of actions, is a solution to the problem, can be determined in time that is polynomial in the qualitative state space, which is much smaller than the original infinite state space. We also report experimental results using a simple generate-and-test planner to illustrate these findings.