Meet Ece Kamar, a senior researcher at Microsoft who works on human-machine collaboration, AI systems in the real world, and issues around bias, robustness, reliability, and transparency in AI. Ece also co-authored the first report in a 100-year study of artificial intelligence, intended to provide a set of reflections about the field as it progresses. The report offers insights on where AI is headed, policy recommendations, and the importance of reflecting on fairness and transparency in the field. Ece believes that it's unlikely that important tasks will ever be fully automated, as human-AI partnerships will be complementary, rather than a relationship of replacement. See how Ece envisions the future of AI, how her academic exploration in college helped shape her career, and how she sees diversity as key to moving the field in a positive direction.

Will Playing Make AI More Human? On March 15, 2016, Google's AI program, AlphaGo, beat world champion Lee Sedol four-to-one in one of the most complex strategy games ever devised – the ancient Chinese game of Go. Since that historic match, Google has released a new version of its AI agent, called AlphaGo Zero, which defeated its predecessor by 100 games to 0. Unlike AlphaGo, which relied on big data, machine learning, and advanced algorithms, AlphaGo Zero started learning Go on its own, from scratch. Starting with a very primitive understanding of the game, AlphaGo Zero created a duplicate of itself, playing itself repeatedly and using what it learned in each match to advance and update its algorithms. Beginning with random play, it took AlphaGo Zero only 40 days to master the game and become the world's best player.

Organisms result from adaptive processes interacting across different time scales. One such interaction is that between development and evolution. Models have shown that development sweeps over several traits in a single agent, sometimes exposing promising static traits. Subsequent evolution can then canalize these rare traits. Thus, development can, under the right conditions, increase evolvability. Here, we report on a previously unknown phenomenon when embodied agents are allowed to develop and evolve: Evolution discovers body plans robust to control changes, these body plans become genetically assimilated, yet controllers for these agents are not assimilated. This allows evolution to continue climbing fitness gradients by tinkering with the developmental programs for controllers within these permissive body plans. This exposes a previously unknown detail about the Baldwin effect: instead of all useful traits becoming genetically assimilated, only traits that render the agent robust to changes in other traits become assimilated. We refer to this as differential canalization. This finding also has implications for the evolutionary design of artificial and embodied agents such as robots: robots robust to internal changes in their controllers may also be robust to external changes in their environment, such as transferal from simulation to reality or deployment in novel environments.

Artificial intelligence makes this possible for the hair color products retailer. The machine-learning algorithm factors in the answers to a 20-question hair coloring quiz more than 4 million shoppers have taken, along with more than 24,0000 product reviews of the retailer's 50 SKUs. It also factors in a shopper's repeat purchase rate, net promoter score and hundreds of thousands of shopper and customer service agent interactions, says Dave King, chief technology officer at Madison Reed. With all of this historical information, Madison Reed's algorithm is the ultimate master colorist that can recommend hair coloring products, King says. When Madison Reed first launched its product recommendation engine, it was coded by humans and did not factor in all of the data points that it does today.

Journal of Parallel and Distributed Computing 104, 19-35.

Our description of logical framing as a process of rational comprehension of perceptual experience by an intelligent agent begins with the definition of "templates" and "objects". Templates are similar to forms and schemata, and objects are similar to perceptual patterns. While both are network-like structures of data, they differ in both content and function. The nodes of an object represent the elements or parts of some external thing, and the links represent the relations between elements. Elements are defined by "descriptive properties" that exist along any number of dimensions (e.g.

Autonomously training interpretable control strategies, called policies, using pre-existing plant trajectory data is of great interest in industrial applications. Fuzzy controllers have been used in industry for decades as interpretable and efficient system controllers. In this study, we introduce a fuzzy genetic programming (GP) approach called fuzzy GP reinforcement learning (FGPRL) that can select the relevant state features, determine the size of the required fuzzy rule set, and automatically adjust all the controller parameters simultaneously. Each GP individual's fitness is computed using model-based batch reinforcement learning (RL), which first trains a model using available system samples and subsequently performs Monte Carlo rollouts to predict each policy candidate's performance. We compare FGPRL to an extended version of a related method called fuzzy particle swarm reinforcement learning (FPSRL), which uses swarm intelligence to tune the fuzzy policy parameters. Experiments using an industrial benchmark show that FGPRL is able to autonomously learn interpretable fuzzy policies with high control performance.

Organization concepts and models are increasingly being adopted for the design and specification of multi-agent systems. Agent organizations can be seen as mechanisms of social order, created to achieve global (or organizational) objectives by more or less autonomous agents. In order to develop a theory on the relation between organizational structures, organizational objectives and the actions of agents fulfilling roles in the organization a theoretical framework is needed to describe organizational structures and actions of (groups of) agents. Current logical formalisms focus on specific aspects of organizations (e.g. power, delegation, agent actions, or normative issues) but a framework that integrates and relates different aspects is missing. Given the amount of aspects involved and the subsequent complexity of a formalism encompassing them all, it is difficult to realize. In this paper, a first step is taken to solve this problem. We present a generic formal model that enables to specify and relate the main concepts of an organization (including, activity, structure, environment and others) so that organizations can be analyzed at a high level of abstraction. However, for some aspects we use a simplified model in order to avoid the complexity of combining many different types of (modal) operators.

The sine-cosine algorithm (SCA) is a new population-based meta-heuristic algorithm. In addition to exploiting sine and cosine functions to perform local and global searches (hence the name sine-cosine), the SCA introduces several random and adaptive parameters to facilitate the search process. Although it shows promising results, the search process of the SCA is vulnerable to local minima/maxima due to the adoption of a fixed switch probability and the bounded magnitude of the sine and cosine functions (from -1 to 1). In this paper, we propose a new hybrid Q-learning sine-cosine- based strategy, called the Q-learning sine-cosine algorithm (QLSCA). Within the QLSCA, we eliminate the switching probability. Instead, we rely on the Q-learning algorithm (based on the penalty and reward mechanism) to dynamically identify the best operation during runtime. Additionally, we integrate two new operations (L\'evy flight motion and crossover) into the QLSCA to facilitate jumping out of local minima/maxima and enhance the solution diversity. To assess its performance, we adopt the QLSCA for the combinatorial test suite minimization problem. Experimental results reveal that the QLSCA is statistically superior with regard to test suite size reduction compared to recent state-of-the-art strategies, including the original SCA, the particle swarm test generator (PSTG), adaptive particle swarm optimization (APSO) and the cuckoo search strategy (CS) at the 95% confidence level. However, concerning the comparison with discrete particle swarm optimization (DPSO), there is no significant difference in performance at the 95% confidence level. On a positive note, the QLSCA statistically outperforms the DPSO in certain configurations at the 90% confidence level.

Current reinforcement learning (RL) methods can successfully learn single tasks but often generalize poorly to modest perturbations in task domain or training procedure. In this work, we present a decoupled learning strategy for RL that creates a shared representation space where knowledge can be robustly transferred. We separate learning the task representation, the forward dynamics, the inverse dynamics and the reward function of the domain, and show that this decoupling improves performance within the task, transfers well to changes in dynamics and reward, and can be effectively used for online planning. Empirical results show good performance in both continuous and discrete RL domains.