37,202 research outputs found
Artificial Intelligence and Surgery: Ethical Dilemmas and Open Issues
Background:
Artificial Intelligence (AI) applications aiming to support surgical decision-making processes are generating novel threats to ethical surgical care. To understand and address these threates, we summarize the main ethical issues that may arise from applying AI to surgery, starting from the Ethics Guidelines for Trustworthy Artificial Intelligence framework recently promoted by the European Commission.
Study Design:
A modified Delphi process has been employed to achieve expert consensus.
Results:
The main ethical issues that arise from applying AI to surgery, described in detail herein, relate to human agency, accountability for errors, technical robustness, privacy and data governance, transparency, diversity, non-discrimination, and fairness. It may be possible to address many of these ethical issues by expanding the breadth of surgical AI research to focus on implementation science.
The potential for AI to disrupt surgical practice suggests that formal digital health education is becoming increasingly important for surgeons and surgical trainees.
Conclusions:
A multidisciplinary focus on implementation science and digital health education is desirable to balance opportunities offered by emerging AI technologies and respect for the ethical principles of a patient-centric philosophy
Best-first heuristic search for multicore machines
To harness modern multicore processors, it is imperative to develop parallel versions of fundamental algorithms. In this paper, we compare different approaches to parallel best-first search in a shared-memory setting. We present a new method, PBNF, that uses abstraction to partition the state space and to detect duplicate states without requiring frequent locking. PBNF allows speculative expansions when necessary to keep threads busy. We identify and fix potential livelock conditions in our approach, proving its correctness using temporal logic. Our approach is general, allowing it to extend easily to suboptimal and anytime heuristic search. In an empirical comparison on STRIPS planning, grid pathfinding, and sliding tile puzzle problems using 8-core machines, we show that A*, weighted A* and Anytime weighted A* implemented using PBNF yield faster search than improved versions of previous parallel search proposals
Real-time Planning as Decision-making Under Uncertainty
In real-time planning, an agent must select the next action to take within a fixed time bound.
Many popular real-time heuristic search methods approach this by expanding nodes using time-limited A* and selecting the action leading toward the frontier node with the lowest f value. In this thesis, we reconsider real-time planning as a problem of decision-making under uncertainty. We treat heuristic values as uncertain evidence and we explore several backup methods for aggregating this evidence. We then propose a novel lookahead strategy that expands nodes to minimize risk, the expected regret in case a non-optimal action is chosen. We evaluate these methods in a simple synthetic benchmark and the sliding tile puzzle and find that they outperform previous methods. This work illustrates how uncertainty can arise even when solving deterministic planning problems, due to the inherent ignorance of time-limited search algorithms about those portions of the state space that they have not computed, and how an agent can benefit from explicitly meta-reasoning about this uncertainty
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EASe : integrating search with learned episodes
Weak methods are insufficient to solve complex problems. Constrained weak methods, like hill-climbing, search too little of the problem space. Unconstrained weak methods, like breadth-first search, are intractable. Fortunately, through the integration of multiple weak methods more powerful problem solvers can be created. We demonstrate that augmenting a weak constrained search method with episodes provides a tractable method for solving a large class of problems. We demonstrate that these episodes can be generated using an unconstrained weak method while solving simple problems from a domain. We provide an analytical model of our approach and empirical results from the logic synthesis domain of VLSI design as well as the classic tile-sliding domain
Study and development of techniques for automatic control of remote manipulators
An overall conceptual design for an autonomous control system of remote manipulators which utilizes feedback was constructed. The system consists of a description of the high-level capabilities of a model from which design algorithms are constructed. The autonomous capability is achieved through automatic planning and locally controlled execution of the plans. The operator gives his commands in high level task-oriented terms. The system transforms these commands into a plan. It uses built-in procedural knowledge of the problem domain and an internal model of the current state of the world
Marvin: A Heuristic Search Planner with Online Macro-Action Learning
This paper describes Marvin, a planner that competed in the Fourth
International Planning Competition (IPC 4). Marvin uses
action-sequence-memoisation techniques to generate macro-actions, which are
then used during search for a solution plan. We provide an overview of its
architecture and search behaviour, detailing the algorithms used. We also
empirically demonstrate the effectiveness of its features in various planning
domains; in particular, the effects on performance due to the use of
macro-actions, the novel features of its search behaviour, and the native
support of ADL and Derived Predicates
A Multi-channel Application Framework for Customer Care Service Using Best-First Search Technique
It has become imperative to find a solution to the dissatisfaction in response by mobile
service providers when interacting with their customer care centres. Problems faced with
Human to Human Interaction (H2H) between customer care centres and their customers
include delayed response time, inconsistent solutions to questions or enquires and lack of
dedicated access channels for interaction with customer care centres in some cases.
This paper presents a framework and development techniques for a multi-channel
application providing Human to System (H2S) interaction for customer care centre of a
mobile telecommunication provider. The proposed solution is called Interactive Customer
Service Agent (ICSA). Based on single-authoring, it will provide three media of interaction
with the customer care centre of a mobile telecommunication operator: voice, phone and
web browsing. A mathematical search technique called Best-First Search to generate
accurate results in a search environmen
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