Launched as if from a trebuchet at the end of April, Manor Lords is the latest in a string of explosively successful video games that have been released this year. Indeed, the rise of this unassuming-looking city-builder is arguably more impressive than the enormous launch of Helldivers 2, or the breakout Poker phenomenon Balatro. Developed largely by one person and releasing in an incomplete state, Manor Lords shifted a million copies in its first 24 hours on sale. The scale of Manor Lords' success is remarkable, but contrary to appearances, it hasn't emerged from nowhere. Momentum around the game has been building for years, part of a broader surge in popularity for city-building games in general.

G'day, I'm Lee Hopkins, and this is bcr vidcast edition 108. Three things to consider today: AI and the pharmaceutical industry; will AI kill off too many jobs? Pharmaceutical companies are adopting an'Us WITH Them' approach to AI. 'Augmented intelligence' is the catchphrase that Pamela Spence uses; Pamela is EY's global sector leader for life sciences. Data is being analysed by algorithms, then suggestions are made to human decision-makers. AI is not making decisions by itself.

In order to mitigate the displacement effect on this large portion of the workforce, as well as to help members of the UK's labour pool who have already found themselves out of work, PwC issued a number of recommendations to accompany its report. Most importantly, the firm suggests that the UK Government should invest more in'STEAM' skills that will be most useful to people in this increasingly automated world. While this does mean pushing for schools to focus more on STEM subjects (science, technology, engineering and mathematics), it also means Britain should explore how art and design – the'A' in'STEAM' – can feature at the heart of innovation. It is not solely about educating new labour, though, and PwC also states that governments have a responsibility to encourage workers to continually update and adapt their skills so as to complement what new machines and AI can do. Meanwhile, the UK Government should strengthen the state's safety net for those who find it hard to adjust to technological changes.

ISIS's development was driven by the C4.5, ISIS players learned offline to STEAM's teamwork reasoning is currently However, the RoboCup simulator will evolve for RoboCup-98 toward more humanlike play.

Before Everybody's Gone to the Rapture, indie developer The Chinese Room (TCR) wowed people with Dear Esther. The first-person narrative started as a mod for Half-life 2 in 2008 before the team released it as a standalone game in 2012. At that point, the game sold 16,000 copies on Steam in its first five-and-a-half hours and the team recouped its development costs ( 55,000) in one fell swoop. Fun fact: Original financier Indie Fund proposed releasing the game on PlayStation Network instead of Steam. So this is kind of a four-years-in-the-making homecoming for the game.

Funny, strange, sometimes alarming things can occur when you chat up one of those artificial intelligence bots capable of convincingly chatting back. Look no further than Cleverbot. It's all right there in the parental-advice disclaimer: "things it says may seem inappropriate"; "whatever it says, visitors never talk to a human"; "use with discretion and at YOUR OWN RISK." These web apps can be amusing to screw around with, but what if natural-language AI was baked into a full-fledged video game? That's what Ocelot Society is going for with its upcoming narrative exploration game Event[0], which is now set for a September 14, 2016 release on Steam.

Behavioral game analytics has predominantly been confined to work on single games, which means that the cross-game applicability of current knowledge remains largely unknown. Here four experiments are presented focusing on the relationship between game ownership, time invested in playing games, and the players themselves, across more than 3000 games distributed by the Steam platform and over 6 million players, covering a total playtime of over 5 billion hours. Experiments are targeted at uncovering high-level patterns in the behavior of players focusing on playtime, using frequent itemset mining on game ownership, cluster analysis to develop playtime-dependent player profiles, correlation between user game rankings and, review scores, playtime and game ownership, as well as cluster analysis on Steam games. Within the context of playtime, the analyses presented provide unique insights into the behavior of game players as they occur across games, for example in how players distribute their time across games.

Despite the significant progress in multiagent teamwork, existing research does not address the optimality of its prescriptions nor the complexity of the teamwork problem. Without a characterization of the optimality-complexity tradeoffs, it is impossible to determine whether the assumptions and approximations made by a particular theory gain enough efficiency to justify the losses in overall performance. To provide a tool for use by multiagent researchers in evaluating this tradeoff, we present a unified framework, the COMmunicative Multiagent Team Decision Problem (COM-MTDP). The COM-MTDP model combines and extends existing multiagent theories, such as decentralized partially observable Markov decision processes and economic team theory. In addition to their generality of representation, COM-MTDPs also support the analysis of both the optimality of team performance and the computational complexity of the agents' decision problem. In analyzing complexity, we present a breakdown of the computational complexity of constructing optimal teams under various classes of problem domains, along the dimensions of observability and communication cost. In analyzing optimality, we exploit the COM-MTDP's ability to encode existing teamwork theories and models to encode two instantiations of joint intentions theory taken from the literature. Furthermore, the COM-MTDP model provides a basis for the development of novel team coordination algorithms. We derive a domain-independent criterion for optimal communication and provide a comparative analysis of the two joint intentions instantiations with respect to this optimal policy. We have implemented a reusable, domain-independent software package based on COM-MTDPs to analyze teamwork coordination strategies, and we demonstrate its use by encoding and evaluating the two joint intentions strategies within an example domain.

In a complex, dynamic multiagent setting, coherent team actions are often jeopardized by conflicts in agents' beliefs, plans, and actions. Despite the considerable progress in teamwork research, the challenge of intrateam conflict resolution has remained largely unaddressed. This article presents CONSA, a system we are developing to resolve conflicts using argumentation-based negotiations. CONSA focuses on exploiting the benefits of argumentation in a team setting. Thus, CONSA casts conflict resolution as a team problem, so that the recent advances in teamwork can be brought to bear during conflict resolution to improve argumentation flexibility. Furthermore, because teamwork conflicts sometimes involve past teamwork, teamwork models can be exploited to provide agents with reusable argumentation knowledge. Additionally, CONSA also includes argumentation strategies geared toward benefiting the team, rather than the individual, and techniques to reduce argumentation overhead.

's performance in is driven by's development was driven by the Using Further aspects of multiagent agents could not always quickly locate and agent and team modeling. With respect to learning, as well as arenas of agent and intercept the ball or maintain awareness of teamwork, our previous work was based on team modeling (particularly to recognize positions of teammates and opponents. It then enables team members to make any decisions. Instead, all the decision Yaser Al-Onaizan, Ali Erdem, autonomously reason about coordination making rests with the higher level, Gal A. Kaminka, Stacy C. Marsella, and communication in teamwork, providing implemented in the Given its domain architecture, which takes into account the independence, it also enables reuse across recommendations made by the lower level. 's teamwork reasoning is currently test domain given its substantial also implemented in