Min, Wookhee (North Carolina State University) | Baikadi, Alok (University of Pittsburgh) | Mott, Bradford (North Carolina State University) | Rowe, Jonathan (North Carolina State University) | Liu, Barry (North Carolina State University) | Ha, Eun Young (IBM) | Lester, James (North Carolina State University)
Recent years have seen a growing interest in player modeling, which supports the creation of player-adaptive digital games. A central problem of player modeling is goal recognition, which aims to recognize players’ intentions from observable gameplay behaviors. Player goal recognition offers the promise of enabling games to dynamically adjust challenge levels, perform procedural content generation, and create believable NPC interactions. A growing body of work is investigating a wide range of machine learning-based goal recognition models. In this paper, we introduce GOALIE, a multidimensional framework for evaluating player goal recognition models. The framework integrates multiple metrics for player goal recognition models, including two novel metrics, n-early convergence rate and standardized convergence point . We demonstrate the application of the GOALIE framework with the evaluation of several player goal recognition models, including Markov logic network-based, deep feedforward neural network-based, and long short-term memory network-based goal recognizers on two different educational games. The results suggest that GOALIE effectively captures goal recognition behaviors that are key to next-generation player modeling.
Microsoft researchers have created an artificial intelligence-based system that learned how to get the maximum score on the addictive 1980s video game Ms. Pac-Man, using a divide-and-conquer method that could have broad implications for teaching AI agents to do complex tasks that augment human capabilities. The team from Maluuba, a Canadian deep learning startup acquired by Microsoft earlier this year, used a branch of AI called reinforcement learning to play the Atari 2600 version of Ms. Pac-Man perfectly. Using that method, the team achieved the maximum score possible of 999,990. Doina Precup, an associate professor of computer science at McGill University in Montreal said that's a significant achievement among AI researchers, who have been using various videogames to test their systems but have found Ms. Pac-Man among the most difficult to crack. But Precup said she was impressed not just with what the researchers achieved but with how they achieved it.
If you thought solving a Rubik's cube was difficult, you were right and maths can back you up. A recent study shows that the question of whether a scrambled Rubik's cube of any size can be solved in a given number of moves is what's called NP-complete – that's maths lingo for a problem even mathematicians find hard to solve. To prove that the problem is NP-complete, Massachusetts Institute of Technology researchers Erik Demaine, Sarah Eisenstat, and Mikhail Rudoy showed that figuring out how to solve a Rubik's cube with any number of squares on a side in the smallest number of moves will also give you a solution to another problem known to be NP-complete: the Hamiltonian path problem. That question asks whether there is route that visits each vertex exactly once in a given graph consisting of a collection of vertices connected by edges, like a triangle, pentagram, or the vast connections in a social network such as Facebook. It's reminiscent of the travelling salesperson problem, which aims to find the shortest route that visits several cities only once, probably the most famous NP-complete question of all.
As the non-playable characters (NPCs) of squad-based shooter computer games share a common goal, they should work together in teams and display cooperative behaviours that are tactically sound. Our research examines genetic programming (GP) as a technique to automatically develop effective team behaviours for shooter games. GP has been used to evolve teams capable of defeating a single powerful enemy agent in a number of environments without the use of any explicit team communication. The aim of this paper is to explore the effects of communication on the evolution of effective squad behaviours. Thus, NPCs are given the ability to communicate their perceived information during evolution. The results show that communication between team members enables an improvement in average team effectiveness.