Artifi cial potential fi elds is a technique that use attractive and repelling forces to control e.g. robots, or non player characters in games. We show how this technique may be used in a controller for Super Mario in a way create a human-like playing style. By combining fi elds of progression, opponent avoidance and rewards, we get a controller that tries to collect the rewards and avoid the opponents at the same time as it is progressing towards the goal of the level. We use human test persons to improve the controller further by letting them make pair-wise comparisons with human play recordings, and use the feed-back to calibrate the bot for human-like play. / Student 1: Likith Poovanna Kelapanda Staish Mob: +46735542609 Student 2: Vinay Sudha Ethiraj Mob: +46736135683

Background: Capabilities of Artificial Intelligence (AI) are rapidly advancing, as are its potential applications. Examples of the adoption of AI in social work already exist, but an overview of its manifold uses is lacking. This review aimed to systematically assess the existing research focused on the uses of AI applications in social work practice and to spotlight use-cases yet to be explored. Methods: A scoping review was conducted guided by Arksey and O'Malley's framework and adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis, extension for Scoping Review (PRISMA-ScR). A systematic search was performed using the Scopus database. Eligibility criteria included pre-prints and published articles from January 2000 to April 2023 that emphasized AI implementations in social work practice. No limitations were placed on study design. Data extracted included: article details; country of study; the AI use-case and task; and the specific AI technology employed. Extracted data from all eligible studies were collated using tables and accompanied by narrative descriptive summaries. The review employed CAIMeR (a theory explaining the results of social work interventions) to  pinpoint gaps and highlight novel unexplored applications of AI in social work.  Results: Of the 159 identified articles, 28 satisfied the inclusion criteria. On average, three relevant publications surfaced annually, with approximately 60% hailing from the US. Notably, the absolute majority of the applications of AI were concentrated on predicting or elucidating individual’s health or social condition. Conclusion: Although AI possesses substantial potential, current research into its applications in social work remains surprisingly sparse and averaging a mere three studies annually. The prevailing emphasis of this research is on discerning individual health or social conditions. Given AI's multifaceted capabilities, there exists a substantial opportunity to broaden research into other applications. Informed by the CAIMeR theory, this review identifies several unexplored applications of AI paving the way for future research. / Bakgrund: Utvecklingen inom Artificiell Intelligens (AI) medför betydande potentiella fördelar och utmaningar, vilket understryker behovet för det socialt arbetets praktik att anpassa och ta till sig dess användning. Denna studie undersöker användningen av AI inom socialt arbete genom att kartlägga inom vilka domäner av socialt arbete AI har använts och för vilket syfte. Därtill identifieras forskningsluckor och nya användningsområden för AI med hjälp av CAIMeR teorin. Metod: Genom att använda en scoping review metodik vägledd av Arksey och O'Malleys ramverk och PRISMA-ScR:s riktlinjer, utfördes en systematisk sökning i Scopus fram till april 2023 med fokus på artiklar som diskuterar AI:s implementering i socialt arbete. Resultat: Av 159 artiklar som hittades uppfyllde 28 inkluderingskriterierna. AI har använts flitigt inom socialt arbete, främst för att förutsäga eller diagnostisera individers tillstånd. Forskningsvolymen är begränsad, med ungefär tre studier som genomförts årligen. Slutsats: Trots AI:s potential att förbättra socialt arbete visar nuvarande litteratur en begränsad forskningsvolym om ämnet och ett begränsat användningssätt för AI. Nästan uteslutande koncentrerar sig studierna på användningen av AI för att förutsäga sociala problem eller hälsotillstånd. Studien identifierar ett behov av att utforska AI inom flera användningsområden inom socialt arbete. Med hjälp av CAIMeR-teorin presenterar denna studie flera sådana potentiella användningsområden av AI.