Negative Reinforcement and Backtrack-Points for Recurrent Neural Networks for Cost-Based Abduction

Abduction is the process of proceeding from data describing a set of observations or events, to a set of hypotheses which best explains or accounts for the data. Cost-based abduction (CKA) is an AI formalism in which evidence to be explained is treated as a goal to be proven, proofs have costs based on how much needs to be assumed to complete the proof, and the set of assumptions needed to complete the least-cost proof are taken as the best explanation for the given evidence. In this paper, we introduce two techniques for improving the performance of high order recurrent networks (HORN) applied to cost-based abduction. In the backtrack-points technique, we use heuristics to recognize early that the network trajectory is moving in the wrong direction; we then restore the network state to a previously-stored point, and apply heuristic perturbations to nudge the network trajectory in a different direction. In the negative reinforcement technique, we add hyperedges to the network to reduce the attractiveness of local-minima. We apply these techniques on a 300-hypothesis, 900-rule particularly-difficult instance of CBA

A linear constraint satisfaction approach to cost-based abduction

Abstract Santos Jr, E., A linear constraint satisfaction approach to cost-based abduction, Artificial Intelligence 65 (1994) 1-27. Abduction is the problem of finding the best explanation for a given set of observations. Within AI, this has been modeled as proving the observation by assuming some set of hypotheses. Cost-based abduction associates a cost with each hypothesis. The best proof is the one which assumes the least costly set. Previous approaches to finding the least cost set have formalized cost-based abduction as a heuristic graph search problem. However, efficient admissible heuristics have proven difficult to find. In this paper, we present a new technique for finding least cost sets by using linear constraints to represent causal relationships. In particular, we are able to recast the problem as a 0-1 integer linear programming problem. We can then use the highly efficient optimization tools of operations research yielding a computationally efficient method for solving cost-based abduction problems. Experiments comparing our linear constraint satisfaction approach to standard graph searching methodologies suggest that our approach is superior to existing search techniques in that our approach exhibits an expected-case polynomial run-time growth rate

Parallel versus iterated: comparing population oriented and chained sequential simulated annealing approaches to cost-based abduction

Stochastic search techniques are used to solve NP-hard combinatorial optimization problems. Simulated annealing, genetic algorithms and hybridization of both, all attempt to find the best solution with minimal cost and time. Guided Evolutionary Simulated Annealing is one technique of such hybridization. It is based on evolutionary programming where a number of simulated annealing chains are working in a generation to find the optimum solution for a problem. Abduction is the problem of finding the best explanation to a given set of observations. In AI, this has been modeled by a set of hypotheses that need to be assumed to prove the observation or goal. Cost-Based Abduction (CBA) associates a cost to each hypothesis. It is an example of an NP-hard problem, where the objective is to minimize the cost of the assumed hypotheses to prove the goal. Analyzing the search space of a problem is one way of understanding its nature and categorizing it into straightforward, misleading or difficult for genetic algorithms. Fitness-Distance Correlation and Fitness-Distance plots are helpful tools in such analysis. This thesis examines solving the CBA problem using Simulated Annealing and Guided Evolutionary Simulated Annealing and analyses the Fitness-Distance landscape of some Cost-Based abduction problem instances

When to Trust AI: Advances and Challenges for Certification of Neural Networks

Artificial intelligence (AI) has been advancing at a fast pace and it is now poised for deployment in a wide range of applications, such as autonomous systems, medical diagnosis and natural language processing. Early adoption of AI technology for real-world applications has not been without problems, particularly for neural networks, which may be unstable and susceptible to adversarial examples. In the longer term, appropriate safety assurance techniques need to be developed to reduce potential harm due to avoidable system failures and ensure trustworthiness. Focusing on certification and explainability, this paper provides an overview of techniques that have been developed to ensure safety of AI decisions and discusses future challenges

Integrating Recognition and Decision Making to Close the Interaction Loop for Autonomous Systems

Intelligent systems are becoming increasingly ubiquitous in daily life. Mobile devices are providing machine-generated support to users, robots are coming out of their cages in manufacturing to interact with co-workers, and cars with various degrees of self-driving capabilities operate amongst pedestrians and the driver. However, these interactive intelligent systems\u27 effectiveness depends on their understanding and recognition of human activities and goals, as well as their responses to people in a timely manner. The average person does not follow instructions step-by-step or act in a formulaic manner, but instead varies the order of actions and timing when performing a given task. People explore their surroundings, make mistakes, and may interrupt an activity to handle more urgent matters. The decisions that an autonomous intelligent system makes should account for such noise and variance regardless of the form of interaction, which includes adapting action choices and possibly its own goals.While most people take these aspects of interaction for granted, they are complex and involve many specific tasks that have primarily been studied independently within artificial intelligence. This results in open-loop interactive experiences where the user must perform a fixed input command or the intelligent system performs a hard-coded output response---one of the components of the interaction cannot adapt with respect to the other for longer-term back-and-forth interactions. This dissertation explores how developments in plan recognition, activity recognition, intent recognition, and autonomous planning can work together to develop more adaptive interactive experiences between autonomous intelligent systems and the people around them. In particular, we consider a unifying perspective of recognition algorithms that provides sufficient information to dynamically produce short-term automated planning problems, and we present ways to run these algorithms faster for the real-time needs of interaction. This exploration leads to the introduction of the Planning and Recognition Together Close the Interaction Loop (PReTCIL) framework that serves as a first step towards identifying how we can address the problem of closing the interaction loop, in addition to new questions that need to be considered

LOGIC AND CONSTRAINT PROGRAMMING FOR COMPUTATIONAL SUSTAINABILITY

Computational Sustainability is an interdisciplinary field that aims to develop computational and mathematical models and methods for decision making concerning the management and allocation of resources in order to help solve environmental problems. This thesis deals with a broad spectrum of such problems (energy efficiency, water management, limiting greenhouse gas emissions and fuel consumption) giving a contribution towards their solution by means of Logic Programming (LP) and Constraint Programming (CP), declarative paradigms from Artificial Intelligence of proven solidity. The problems described in this thesis were proposed by experts of the respective domains and tested on the real data instances they provided. The results are encouraging and show the aptness of the chosen methodologies and approaches. The overall aim of this work is twofold: both to address real world problems in order to achieve practical results and to get, from the application of LP and CP technologies to complex scenarios, feedback and directions useful for their improvement

Proceedings of the Automated Reasoning Workshop (ARW 2019)

Preface This volume contains the proceedings of ARW 2019, the twenty sixths Workshop on Automated Rea- soning (2nd{3d September 2019) hosted by the Department of Computer Science, Middlesex University, England (UK). Traditionally, this annual workshop which brings together, for a two-day intensive pro- gramme, researchers from different areas of automated reasoning, covers both traditional and emerging topics, disseminates achieved results or work in progress. During informal discussions at workshop ses- sions, the attendees, whether they are established in the Automated Reasoning community or are only at their early stages of their research career, gain invaluable feedback from colleagues. ARW always looks at the ways of strengthening links between academia, industry and government; between theoretical and practical advances. The 26th ARW is affiliated with TABLEAUX 2019 conference. These proceedings contain forteen extended abstracts contributed by the participants of the workshop and assembled in order of their presentations at the workshop. The abstracts cover a wide range of topics including the development of reasoning techniques for Agents, Model-Checking, Proof Search for classical and non-classical logics, Description Logics, development of Intelligent Prediction Models, application of Machine Learning to theorem proving, applications of AR in Cloud Computing and Networking. I would like to thank the members of the ARW Organising Committee for their advice and assis- tance. I would also like to thank the organisers of TABLEAUX/FroCoS 2019, and Andrei Popescu, the TABLEAUX Conference Chair, in particular, for the enormous work related to the organisation of this affiliation. I would also like to thank Natalia Yerashenia for helping in preparing these proceedings. London Alexander Bolotov September 201