Developing cognitive agents with human-level natural language understanding (NLU) capabilities requires modeling human cognition because natural, unedited utterances regularly contain ambiguities, ellipses, production errors, implicatures, and many other types of complexities. Moreover, cognitive agents must be nimble in the face of incomplete interpretations since even people do not perfectly understand every aspect of every utterance they hear. So, once an agent has reached the best interpretation it can, it must determine how to proceed – be that acting upon the new information directly, remembering an incomplete interpretation and waiting to see what happens next, seeking out information to fill in the blanks, or asking its interlocutor for clarification. The reasoning needed to support NLU extends far beyond language itself, including, non-exhaustively, the agent’s understanding of its own plans and goals; its dynamic modeling of its interlocutor’s knowledge, plans, and goals, all guided by a theory of mind; its recognition of diverse aspects human behavior, such as affect, cooperative behavior, and the effects of cognitive biases; and its integration of linguistic interpretations with its interpretations of other perceptive inputs, such as simulated vision and non-linguistic audition. Considering all of these needs, it seems hardly possible that fundamental NLU will ever be achieved through the kinds of knowledge-lean text-string manipulation being pursued by the mainstream natural language processing (NLP) community. Instead, it requires a holistic approach to cognitive modeling of the type we are pursuing in a paradigm called OntoAgent.

Morality is a fundamentally human trait which permeates all levels of human society, from basic etiquette and normative expectations of social groups, to formalized legal principles upheld by societies. Hence, future interactive AI systems, in particular, cognitive systems on robots deployed in human settings, will have to meet human normative expectations, for otherwise these system risk causing harm. While the interest in “machine ethics” has increased rapidly in recent years, there are only very few current efforts in the cognitive systems community to investigate moral and ethical reasoning. And there is currently no cognitive architecture that has even rudimentary moral or ethical competence, i.e., the ability to judge situations based on moral principles such as norms and values and make morally and ethically sound decisions. We hence argue for the urgent need to instill moral and ethical competence in all cognitive system intended to be employed in human social contexts.

Neural networks have been successfully implemented in a plethora of prediction tasks ranging from speech interpretation to facial recognition. Because of groundbreaking work in optimization techniques (such as batch normalization, Ioffe and Szegedy (2015)) and model architecture (convolutional, deep belief, and LSTM networks), it is now tractable to use deep neural networks to effectively learn a better feature representation compared to handcrafted methods. 1 One area where such methods have not been utilized is the space of adversarial multiagent systems (for example, multiple independent players in competition), specifically when the multiagent behavior comes in the form of trajectories. There are two reasons for this: i) procuring large volumes of data where deep methods are effective is difficult to obtain, and ii) forming an initial representation of the raw trajectories so that deep neural networks are effective is challenging. In this paper, we explore the effectiveness of deep neural networks on a large volume of basketball tracking data, which contains the x, y locations of multiple agents (players) in an adversarial domain (game). To thoroughly explore this problem, we focus on the following task: "given the trajectories of the players and ball in the previous five seconds, can we accurately predict the likelihood that a player with position/role X will make the shot?"

Recent progress in artificial intelligence, especially in the area of deep learning, has been breath-taking. This is very encouraging for anyone interested in the field, yet the true progress towards human-level artificial intelligence is much harder to evaluate. The evaluation of artificial intelligence is a very difficult problem for a number of reasons. For example, the lack of consensus on the basic desiderata necessary for intelligent machines is one of the primary barriers to the development of unified approaches towards comparing different agents. José Hernández-Orallo or Kristinn R. Thórisson to name a few), the area would benefit from more attention from the AI community.

A new amortized variance-reduced gradient (AVRG) algorithm was developed in [1], which has constant storage requirement in comparison to SAGA and balanced gradient computations in comparison to SVRG. One key advantage of the AVRG strategy is its amenability to decentralized implementations. In this work, we show how AVRG can be extended to the network case where multiple learning agents are assumed to be connected by a graph topology. In this scenario, each agent observes data that is spatially distributed and all agents are only allowed to communicate with direct neighbors. Moreover, the amount of data observed by the individual agents may differ drastically. For such situations, the balanced gradient computation property of AVRG becomes a real advantage in reducing idle time caused by unbalanced local data storage requirements, which is characteristic of other reduced-variance gradient algorithms. The resulting diffusion-AVRG algorithm is shown to have linear convergence to the exact solution, and is much more memory efficient than other alternative algorithms. In addition, by using a mini-batch strategy, it is shown that diffusion-AVRG is more computationally efficient than exact diffusion or EXTRA while maintaining almost the same amount of communications.

We study the computational complexity of candidate control in elections with few voters, that is, we consider the parameterized complexity of candidate control in elections with respect to the number of voters as a parameter. We consider both the standard scenario of adding and deleting candidates, where one asks whether a given candidate can become a winner (or, in the destructive case, can be precluded from winning) by adding or deleting few candidates, as well as a combinatorial scenario where adding/deleting a candidate automatically means adding or deleting a whole group of candidates. Considering several fundamental voting rules, our results show that the parameterized complexity of candidate control, with the number of voters as the parameter, is much more varied than in the setting with many voters.

An Australian man was taken into custody Saturday for allegedly acting as an economic agent for North Korea and attempting to sell missile parts, military intelligence and coal on the black market. The Australian Federal Police arrested Chan Han Choi, 59, in Sydney and charged him with brokering sales of weapons of mass destruction, according to the Australian Broadcasting Corporation. It is the first time a charge of this kind has been leveled against anyone in Australia. The sales would violate Australian and United Nations sanctions. "We believe this man participated in discussions about the sale of missile componentry from North Korea to other entities abroad as another attempt to try and raise revenue for the government in North Korea, again in breach of the sanctions," said Australian Federal Police Assistant Commissioner Neil Gaughan in a statement.

In this contributed article, Sharmistha Sarkar of India based Progressive Markets, highlights a handful of compelling technology advancements that are helping to drive the evolution of artificial intelligence. Industry is expected to grow at a CAGR of 46.5% from 2017 to 2025. The market is growing fast due to improved productivity through AI, its diversified application areas, and big data integration drive....

Cooperative multi-agent systems can be naturally used to model many real world problems, such as network packet routing and the coordination of autonomous vehicles. There is a great need for new reinforcement learning methods that can efficiently learn decentralised policies for such systems. To this end, we propose a new multi-agent actor-critic method called counterfactual multi-agent (COMA) policy gradients. COMA uses a centralised critic to estimate the Q-function and decentralised actors to optimise the agents' policies. In addition, to address the challenges of multi-agent credit assignment, it uses a counterfactual baseline that marginalises out a single agent's action, while keeping the other agents' actions fixed. COMA also uses a critic representation that allows the counterfactual baseline to be computed efficiently in a single forward pass. We evaluate COMA in the testbed of StarCraft unit micromanagement, using a decentralised variant with significant partial observability. COMA significantly improves average performance over other multi-agent actor-critic methods in this setting, and the best performing agents are competitive with state-of-the-art centralised controllers that get access to the full state.

As a digital analyst or marketer, you know the importance of analytical decision making. Go to any industry conference, blog, meet up, or even just read the popular press, and you will hear and see topics like machine learning, artificial intelligence, and predictive analytics everywhere. Because many of us don't come from a technical/statistical background, this can be both a little confusing and intimidating. But don't sweat it, in this post, I will try to clear up a some of this confusion by introducing a simple, yet powerful framework – the intelligent agent – which will help link these new ideas with familiar tools and concepts like A/B Testing and Optimization. Note: the intelligent agent framework is used as the guiding principle in Russell and Norvig's excellent text Artificial Intelligence: A Modern Approach – it's an awesome book, and I recommend anyone who wants to learn more to go get a copy or check out their online AI course.