We study liquid democracy, a collective decision making paradigm that allows voters to transitively delegate their votes, through an algorithmic lens. In our model, there are two alternatives, one correct and one incorrect, and we are interested in the probability that the majority opinion is correct. Our main question is whether there exist delegation mechanisms that are guaranteed to outperform direct voting, in the sense of being always at least as likely, and sometimes more likely, to make a correct decision. Even though we assume that voters can only delegate their votes to better-informed voters, we show that local delegation mechanisms, which only take the local neighborhood of each voter as input (and, arguably, capture the spirit of liquid democracy), cannot provide the foregoing guarantee. By contrast, we design a non-local delegation mechanism that does provably outperform direct voting under mild assumptions about voters.

The paper considers a distributed version of deep reinforcement learning (DRL) for multi-agent decision-making process in the paradigm of federated learning. Since the deep neural network models in federated learning are trained locally and aggregated iteratively through a central server, frequent information exchange incurs a large amount of communication overheads. Besides, due to the heterogeneity of agents, Markov state transition trajectories from different agents are usually unsynchronized within the same time interval, which will further influence the convergence bound of the aggregated deep neural network models. Therefore, it is of vital importance to reasonably evaluate the effectiveness of different optimization methods. Accordingly, this paper proposes a utility function to consider the balance between reducing communication overheads and improving convergence performance. Meanwhile, this paper develops two new optimization methods on top of variation-aware periodic averaging methods: 1) the decay-based method which gradually decreases the weight of the model's local gradients within the progress of local updating, and 2) the consensus-based method which introduces the consensus algorithm into federated learning for the exchange of the model's local gradients. This paper also provides novel convergence guarantees for both developed methods and demonstrates their effectiveness and efficiency through theoretical analysis and numerical simulation results.

Motivation: Several accurate deep learning models have been proposed to predict drug-target affinity (DTA). However, all of these models are black box hence are difficult to interpret and verify its result, and thus risking acceptance. Explanation is necessary to allow the DTA model more trustworthy. Explanation with counterfactual provides human-understandable examples. Most counterfactual explanation methods only operate on single input data, which are in tabular or continuous forms. In contrast, the DTA model has two discrete inputs. It is challenging for the counterfactual generation framework to optimize both discrete inputs at the same time. Results: We propose a multi-agent reinforcement learning framework, Multi-Agent Counterfactual Drug-target binding Affinity (MACDA), to generate counterfactual explanations for the drug-protein complex. Our proposed framework provides human-interpretable counterfactual instances while optimizing both the input drug and target for counterfactual generation at the same time. The result on the Davis dataset shows the advantages of the proposed MACDA framework compared with previous works.

A desirable goal for autonomous agents is to be able to coordinate on the fly with previously unknown teammates. Known as "ad hoc teamwork", enabling such a capability has been receiving increasing attention in the research community. One of the central challenges in ad hoc teamwork is quickly recognizing the current plans of other agents and planning accordingly. In this paper, we focus on the scenario in which teammates can communicate with one another, but only at a cost. Thus, they must carefully balance plan recognition based on observations vs. that based on communication. This paper proposes a new metric for evaluating how similar are two policies that a teammate may be following - the Expected Divergence Point (EDP). We then present a novel planning algorithm for ad hoc teamwork, determining which query to ask and planning accordingly. We demonstrate the effectiveness of this algorithm in a range of increasingly general communication in ad hoc teamwork problems.

The COVID pandemic may be receding, but it has left a mark on across multiple aspects of our lives. From mask mandates to travel restrictions, we chafe at some of the changes – but in the business world the use of artificial intelligence (AI) systems has dramatically expanded in the past year. This was probably inevitable – but AI brought advantages in coping with the pandemic for companies that could make use of it, and the expansion accelerated. AI has found its place in a huge range of applications, at both the front and back end of businesses. It’s prevalent in software management and data systems, as well as in communications, where AI systems filter emails and conduct robochats. And this has not been ignored by Wall Street. Analysts say that plenty of compelling investments can be found within this space. With this in mind, we’ve opened up TipRanks’ database, and pulled two stocks which are stand to benefit from AI technology. Importantly, both have amassed enough bullish calls from analysts to be given “Strong Buy” consensus ratings. Nuance Communications (NUAN) We’ll start with Nuance, a company in the communications software niche. This Massachusetts-based company offers solutions for business clients in the healthcare and customer service industries, with products that enhance speech recognition, telephone call steering systems, automated phone directories, medical transcription, and optical character recognition. It’s a full range of AI-powered, cloud communications software, applied in real time. Nuance’s flagship product, the Dragon Ambient eXperience (DAX) is marketed to the healthcare industry, where it uses AI to automate the paperwork burdens on physician practices and hospitals. This streamlines operations allow doctors more time and resources to spend on patients, and provides greater satisfaction to health care providers and users. The applications of Nuance’s product and solution lines to the current environment is clear: when the pandemic locked down so many people at home, businesses still had to maintain their customer-facing systems, and software automation, based on AI tech, made that possible with fewer personnel. Since the pandemic started last winter, the company seen its shares grow tremendously, up 205% in the last 12 months, far outpacing the overall stock market. The most recent quarterly report, for fiscal Q1, showed quarterly revenues above the forecast at $81.4 million. EPS showed a net loss, as expected, but at 27 cents the loss was a 28% sequential improvement from Q3. The company’s balance sheet is strong, with zero debt, $256 million cash on hand, and a credit facility up to $50 million. The company’s most recent quarterly report, for fiscal Q1, beat the forecasts on both the top and bottom lines. Earnings beat expectations by 11%, coming in at 20 cents per share, while revenues of $345.8 million were a modest 2% above the estimates. As a result, operating cash flow grew 22% year-over-year, to $54.6 million for the quarter. Among the bulls is 5-star analyst Daniel Ives, of Wedbush, who rates NUAN shares an Outperform (i.e. Buy), and his $65 price target implies an upside potential of ~44%. (To watch Ives’ track record, click here) "We believe Nuance overall continues to be laser focused on building a global cloud healthcare and AI driven business with growing ARR and a sustainable revenue/ earnings stream going forward with larger deals in the field as more hospital- wide deployments shift to the cloud are playing out and gaining further momentum based on our checks," Ives opined. The analyst added, "From a valuation/ SOTP perspective, we believe over time the DAX business alone could be worth between $3 billion to $4 billion to NUAN's stock as this AI next generation platform represents a potential paradigm changer for hospitals/healthcare clinics/specialists over the coming years." Ives is no outlier on Nuance, as shown by the unanimous Strong Buy analyst consensus on the stock. Nuance has received 6 recent reviews, and all are to Buy. The shares are trading for $45.20, and the $59.67 average price target suggests a 32% one-year upside. (See NUAN stock analysis on TipRanks) Dynatrace, Inc. (DT) The second AI stock we’ll look at, Dynatrace, is another cloud software company – but Dynatrace’s products are designed to power business data. The company’s AI platform brings intelligent automation to network management and cloud monitoring. DT’s platform allows for cloud automation, business analytics, digital experience, application security, applications and microservices, and infrastructure monitoring. It’s sold as a one-stop-shop for network and system managers seeking an intelligent software agent. Dynatrace’s shares have been showing consistent growth over a long term. The stock is up a robust 133% in the past 12 months, and revenues have also been growing over that period. In the most recent report, for Q3 fiscal year 2021, the company showed $182.9 million in top-line revenue, beating the forecast by ~6% and growing 27% year-over-year. EPS came in at 6 cents, flat from Q2 and far better than the break-even reported for the year-ago quarter. Three key metrics stand out in the quarterly report, and both for the right reasons. Subscription revenue grew 33% year-over-year, to reach $170.3 million, and annual recurring revenue (ARR) – which is an important predictor of future performance – grew 35% yoy and came in at $722 million. At the same time, license revenue dropped by more than 93%, to just $300,000. Taken all together, these results point toward a strong shift toward recurring cloud customers – a common trend in the software space. Needham’s 5-star analyst Jack Andrews has been closely following Dynatrace, and he believes DT’s AI products may replace incumbent tools as customers expand to additional modules. “Embedded AIOps and automation creates a compelling value proposition… Compared to competitors in the market, DT's AI Engine is embedded within its core platform and can be levered across the portfolio to deliver answers from data. Moreover, its One Agent technology automatically discovers high-fidelity data from applications and thus can map the billions of dependencies in complex environments," Andrews said. The analyst summed up, "In our view, DT is well-positioned to serve as a single source of truth that can help users trace a line between written code and business outcomes (i.e. BizDevSecOps)." Andrews named Dynatrace as a top pick, and in line with this upbeat assessment, the analyst rates the stock a Buy along with a $66 price target. Ivestors stand to pocket ~28% gain should the analyst's thesis play out. (To watch Andrews’ track record, click here) Once again, we’re looking at a stock who strong performance has inspired unanimity from the Wall Street analysts. DT shares have 13 Buy reviews, for a Strong Buy consensus rating. The stock sells for $51.76 and its $59.69 average price target suggests ~15% upside from that level. (See DT stock analysis on TipRanks) To find good ideas for AI stocks trading at attractive valuations, visit TipRanks’ Best Stocks to Buy, a newly launched tool that unites all of TipRanks’ equity insights. Disclaimer: The opinions expressed in this article are solely those of the featured analysts. The content is intended to be used for informational purposes only. It is very important to do your own analysis before making any investment.

James Hendler is the Director of the Institute for Data Exploration and Applications and the Tetherless World Professor of Computer, Web and Cognitive Sciences at RPI. He also is acting director of the RPI-IBM Artificial Intelligence Research Collaboration and serves on the Board of the UK's charitable Web Science Trust. Hendler has authored over 400 books, technical papers and articles in the areas of Semantic Web, artificial intelligence, agent-based computing and high-performance processing. Hendler was the recipient of a 1995 Fulbright Foundation Fellowship, is a former member of the US Air Force Science Advisory Board, and is a Fellow of the AAAI, BCS, the IEEE, the AAAS and the ACM. He is also the former Chief Scientist of the Information Systems Office at the US Defense Advanced Research Projects Agency (DARPA) and was awarded a US Air Force Exceptional Civilian Service Medal in 2002.

We consider a multi-stage facility reallocation problems on the real line, where a facility is being moved between time stages based on the locations reported by $n$ agents. The aim of the reallocation algorithm is to minimise the social cost, i.e., the sum over the total distance between the facility and all agents at all stages, plus the cost incurred for moving the facility. We study this problem both in the offline setting and online setting. In the offline case the algorithm has full knowledge of the agent locations in all future stages, and in the online setting the algorithm does not know these future locations and must decide the location of the facility on a stage-per-stage basis. We derive the optimal algorithm in both cases. For the online setting we show that its competitive ratio is $(n+2)/(n+1)$. As neither of these algorithms turns out to yield a strategy-proof mechanism, we propose another strategy-proof mechanism which has a competitive ratio of $(n+3)/(n+1)$ for odd $n$ and $(n+4)/n$ for even $n$, which we conjecture to be the best possible. We also consider a generalisation with multiple facilities and weighted agents, for which we show that the optimum can be computed in polynomial time for a fixed number of facilities.

The ability to communicate intention enables decentralized multi-agent robots to collaborate while performing physical tasks. In this work, we present spatial intention maps, a new intention representation for multi-agent vision-based deep reinforcement learning that improves coordination between decentralized mobile manipulators. In this representation, each agent's intention is provided to other agents, and rendered into an overhead 2D map aligned with visual observations. This synergizes with the recently proposed spatial action maps framework, in which state and action representations are spatially aligned, providing inductive biases that encourage emergent cooperative behaviors requiring spatial coordination, such as passing objects to each other or avoiding collisions. Experiments across a variety of multi-agent environments, including heterogeneous robot teams with different abilities (lifting, pushing, or throwing), show that incorporating spatial intention maps improves performance for different mobile manipulation tasks while significantly enhancing cooperative behaviors.

Multi-robot task allocation is one of the most fundamental classes of problems in robotics and is crucial for various real-world robotic applications such as search, rescue and area exploration. We consider the Single-Task robots and Multi-Robot tasks Instantaneous Assignment (ST-MR-IA) setting where each task requires at least a certain number of robots and each robot can work on at most one task and incurs an operational cost for each task. Our aim is to consider a natural computational problem of allocating robots to complete the maximum number of tasks subject to budget constraints. We consider budget constraints of three different kinds: (1) total budget, (2) task budget, and (3) robot budget. We provide a detailed complexity analysis including results on approximations as well as polynomial-time algorithms for the general setting and important restricted settings.

Motivational digital systems offer capabilities to engage and motivate end-users to foster behavioral changes towards a common goal. In general these systems use gamification principles in non-games contexts. Over the years, gamification has gained consensus among researchers and practitioners as a tool to motivate people to perform activities with the ultimate goal of promoting behavioural change, or engaging the users to perform activities that can offer relevant benefits but which can be seen as unrewarding and even tedious. There exists a plethora of heterogeneous application scenarios towards reaching the common good that can benefit from gamification. However, an open problem is how to effectively combine multiple motivational campaigns to maximise the degree of participation without exposing the system to counterproductive behaviours. We conceive motivational digital systems as multi-agent systems: self-adaptation is a feature of the overall system, while individual agents may self-adapt in order to leverage other agents' resources, functionalities and capabilities to perform tasks more efficiently and effectively. Consequently, multiple campaigns can be run and adapted to reach common good. At the same time, agents are grouped into micro-communities in which agents contribute with their own social capital and leverage others' capabilities to balance their weaknesses. In this paper we propose our vision on how the principles at the base of the autonomous and multi-agent systems can be exploited to design multi-challenge motivational systems to engage smart communities towards common goals. We present an initial version of a general framework based on the MAPE-K loop and a set of research challenges that characterise our research roadmap for the implementation of our vision.