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The prevailing methodology for integrating decision making and ethics is to modify autonomous systems in an ad hoc way to incorporate moral sensibility. However, these provisional modifications often lead to behavior that jeopardizes the intentions of developers or the values of stakeholders. We propose a novel approach for building moral autonomous systems that optimally completes a task and follows an ethical framework by decoupling ethical compliance from task completion. This paper offers a formal definition of our approach along with its key properties, an example based on prima facie duties, and a demonstration that uses our open source library.

ECAI 2020

An Integrated Approach to Moral Autonomous Systems

morality

autonomous systems

decision making

markov decision processes

planning

ethics

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

poster

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

Contracts are the cornerstone of legal agreements in the industry. As essential legal documents, contracts are subject to negotiations, demanding extensive analysis and evaluation efforts. Although the emergence of machine learning has enabled assistance tools for text analysis tasks, the specificity and constraints of each
 business context remain obstacles for the automation of contracts evaluation. In this paper, we propose an AutoML based approach for automated negotiation assistance that uses expert annotated contracts and the business-specific knowledge of acceptance policies. Driven by policies rules, our approach generates a classification process composed of hierarchies of complementary classifiers, each being automatically prepared according, but not limited to, feature extraction, learning model and data granularity. Experiments conducted on real-world service contracts have yielded promising results.

Toward a Generic AutoML-Based Assistant for Contracts Negotiation

Malaria is one of the primary causes of death in Mozambique, responsible for an estimated 15,000 deaths in 2017. Human-mediated parasite movement combined with spatial and seasonal changes in transmission threatens the success of malaria interventions by reintroducing parasites to areas targeted for elimination. In this study we used pseudonymized and aggregated call detail records from a sample of over 8.5 million subscribers of a mobile phone operator in Mozambique to quantify human movements for four months in 2017. We calculated parasite importation and exportation rates to identify potential net sinks and sources of malaria across districts. We used a community structure algorithm to explore the connectedness of provinces in Mozambique, and we calculated parasite mobility between urban and rural areas. This work will help provide evidence to inform malaria elimination strategies regionally.

Malaria parasite mobility in Mozambique estimated using mobile phone records

In AI and ML, several methods rely on stochasticity or randomization: mutation and crossover in evolutionary algorithms; dropout and stochastic gradient descent in deep learning; stochasticity in stochastic local search (SLS); and randomization in systematic search. SLS algorithms, which we study here, are competitive in solving computationally hard problems such as satisfiability (SAT), sparse signal recovery, scheduling, and most probable explanations in Bayesian networks (BNs). Essentially, SLS algorithms are greedy optimizers that also make random moves in order to avoid getting trapped in local but non-global optima. 
 
 Clearly, SLS is a well-established idea in AI. However, SLS is sometimes underutilized and there are certain potential difficulties when applying SLS. This highlight showcases recent developments that address some of these difficulties - related to ML, formalization with Markov chains, and setting hyperparameters - and briefly points to future research directions with potential.

Stochastic Local Search and Machine Learning: From Theory to Applications and Vice Versa

In this paper, we identify a series of problems to track and understand what AI is capable of, surveying some previous initiatives. We present the AIcollaboratory, a data-driven framework to collect and explore data about AI results, progress and ultimately capabilities, being developed in the context of AI Watch, the European Commission (EC) knowledge service to monitor the development, uptake and impact of AI in Europe. We close the paper with some challenges for the community emerging around the AIcollaboratory.

Tracking AI: The Capability is (Not) Near

Ontologies are widely used to formally represent abstract domain knowledge. Logic reasoning ensures the logical consistency of ontologies, and infers knowledge implicitly encoded in ontologies. It has been shown both theoretically and empirically that for large and complex ontologies, reasoning is still time-consuming and resource-intensive. Meta-reasoning exploits machine learning techniques to tackle the important problems of understanding the source of reasoning hardness and to predict reasoning efficiency, with the overall goal of improving reasoning efficiency. In this paper, we highlight recent advances in meta-reasoning for Semantic Web ontologies, briefly present technical innovations and results, and discuss important problems for future research.

Towards Meta-reasoning for Ontologies: A Roadmap

The WikiTalk open-domain dialogue system performs Wikipedia-based talking. This paper highlights the potential for a new Wikipedia-based listening capability. The aim is to enable social robots to follow the changing topics in human-human conversations, opening the possibility for robots to join in human conversations by  making topically relevant contributions at appropriate points.

WikiTalk and WikiListen: Towards listening robots that can join in conversations with topically relevant contributions

Recent advances in language model (LM) pre-training from large- scale corpora have shown to improve various natural language processing tasks. They achieve performances comparable to non-expert humans on the GLUE benchmark for natural language understanding (NLU). While the improvement of the different contextualized representations comes from (i) the usage of more and more data, (ii) changing the types of lexical pre-training tasks or (iii) increasing the model size, NLU is more than memorizing word co-occurrences. But how much world knowledge and common sense can those language model capture? How much can those models infer from just the lexical information? To overcome this problem, some approaches include semantic information in the training process. We highlight existing approaches to combine different types of semantics with language models during the pre-training or fine-tuning phase

Enriching Language Models with Semantics

Lifted algorithms use representatives for groups of indistinguishable objects to efficiently perform inference. Standard lifted algorithms like first-order variable elimination or first-order knowledge compilation, compute answers to marginal queries of single random variables or events in a lifted way using representatives. But, queries containing a set of indistinguishable random variables may lead to groundings, something that lifting tries to avoid. This paper presents parameterised queries as a means to avoid groundings, applying the lifting idea to queries. Parameterised queries enable lifted algorithms to compute answers faster, while compactly representing queries and answers.

Lifting Queries for Lifted Inference

Agent-Based Models (ABMs) can be used to numerically simulate highly non-linear phenomena that emerge from local interactions of multiple, independent entities. ABMs are often used to understand dynamic processes such as tissue regeneration in biomedicine, in which group-level patterns and space-time constraints are of interest. High fidelity ABMs, especially those in biomedical applications, usually have a large number of parameters, creating substantial uncertainty and high dimensionality at both local and global levels. Uncertainty
 analysis (output variance estimation) and sensitivity analysis are essential steps in investigating model robustness. In addition to allocating uncertainty to each parameter, sensitivity analysis can also be used to reduce ABM dimensionality for effective model calibration and optimization. I discuss common sensitivity analysis methods that have been used to decrease the number of parameters in complex ABMs, highlighting Garg et al. (2019)’s paper, where random forests – a non-parametric ensemble algorithm – are used as a sensitivity analysis method for ranking parameters in a biomedical ABM.

Sensitivity Analysis for Dimensionality Reduction in Agent-Based Modeling

A picture is worth a thousand words”, the adage reads. However, pictures cannot replace words in terms of their ability to efficiently convey clear (mostly) unambiguous and concise knowledge. Images and text, indeed, reveal different and complementary information that, if combined, result in more information than the
 sum of that contained in the single media. The combination of visual and textual information can be obtained by linking the entities mentioned in the text with those shown in the pictures. To further integrate this with agent background knowledge, an additional step is necessary. That is, either finding the entities in the agent knowledge base that correspond to those mentioned in the text or shown in the
 picture or, extending the knowledge base with the newly discovered entities. We call this complex task Visual-Textual-Knowledge Entity Linking (VTKEL). In this paper, we precisely define the VTKEL task and present two datasets composed of 1k and 30k pictures, annotated with visual and textual entities, and linked to the YAGO ontology. Successively, we develop the first unsupervised algorithm for the solution of the VTKEL task. The evaluation of the algorithm shows promising results on both 1k and 30k VTKEL datasets.

An Integrated Approach to Moral Autonomous Systems

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