Predicting A Better Future With Swarm Intelligence

#artificialintelligence

Have you put a bet on the FIFA World Cup? If yes, the chances are you've made a pretty educated guess, right? You know which team has the strongest players or most favourable odds. Or maybe you've put some cash on your country's team, (which normally I'd avoid England, but given their recent performance, I could be wrong to!) Either way, you might be best casting your bets in line with San Francisco based Unanimous AI. They use a technology called Swarm AI - algorithms modelled on swarms in nature that amplifies human intelligence.


Leveraging human knowledge in tabular reinforcement learning: A study of human subjects

arXiv.org Artificial Intelligence

Reinforcement Learning (RL) can be extremely effective in solving complex, real-world problems. However, injecting human knowledge into an RL agent may require extensive effort and expertise on the human designer's part. To date, human factors are generally not considered in the development and evaluation of possible RL approaches. In this article, we set out to investigate how different methods for injecting human knowledge are applied, in practice, by human designers of varying levels of knowledge and skill. We perform the first empirical evaluation of several methods, including a newly proposed method named SASS which is based on the notion of similarities in the agent's state-action space. Through this human study, consisting of 51 human participants, we shed new light on the human factors that play a key role in RL. We find that the classical reward shaping technique seems to be the most natural method for most designers, both expert and non-expert, to speed up RL. However, we further find that our proposed method SASS can be effectively and efficiently combined with reward shaping, and provides a beneficial alternative to using only a single speedup method with minimal human designer effort overhead.


Predicting A Better Future With Swarm Intelligence Big Cloud Recruitment

#artificialintelligence

Have you put a bet on the FIFA World Cup? If yes, the chances are you've made a pretty educated guess, right? You know which team has the strongest players or most favourable odds. Or maybe you've put some cash on your country's team, (which normally I'd avoid England, but given their recent performance, I could be wrong to!) Either way, you might be best casting your bets in line with San Francisco based Unanimous AI. They use a technology called Swarm AI – algorithms modelled on swarms in nature that amplifies human intelligence.


Do Hard SAT-Related Reasoning Tasks Become Easier in the Krom Fragment?

AAAI Conferences

Many AI-related reasoning problems are based on the problem of satisfiability (SAT). While SAT itself becomes easy when restricting the structure of the formulas in a certain way, this is not guaranteed for more involved reasoning problems. In this work, we focus on reasoning tasks in the areas of belief revision and logic-based abduction and show that in some cases the restriction to Krom formulas (i.e., formulas in CNF where clauses have at most two literals) decreases the complexity, while in others it does not. We thus also consider additional restrictions to Krom formulas towards a better identification of the tractability frontier of such problems.


Q-Decomposition for Reinforcement Learning Agents

AAAI Conferences

The paper explores a very simple agent design method called Q-decomposition, wherein a complex agent is built from simpler subagents. Each subagent has its own reward function and runs its own reinforcement learning process. It supplies to a central arbitrator the Q-values (according to its own reward function) for each possible action. The arbitrator selects an action maximizing the sum of Q-values from all the subagents. This approach has advantages over designs in which subagents recommend actions. It also has the property that if each subagent runs the Sarsa reinforcement learning algorithm to learn its local Q-function, then a globally optimal policy is achieved.