The field of AI is vast that there’s always room for small scale research and inquiry. Utility of AI is key, but the potential applications are broad, and intelligence is a spectrum.
Fundamental Combinatronics, a collective with no current funding, is engaged in a project to develop “adaptive AI” for a set of consumer-oriented, combinatorial game products. The requirements are distinct from real-world applications.
We can’t compete with the major players in terms of resources, and we’re late to the party in terms of Machine Learning and Neural Networks, and, because the AI is for a consumer, mobile game which carries significant restrictions in terms of the bounding rationality (networking cannot be assumed; software volume is measured in megabytes; memory is restricted to lowest-common-denominator consumer-grade devices with non-specialized processors.) For these reasons, we
re going the opposite direction of current industry trends--the good-old "boring stuff".
Because the automata only need to outperform the average/above-average human player, an old-school, heuristic approach is feasible. (Fun also, because it involves solving non-trivial, partisan Sudoku games in a Combinatorial Game Theory sense, a type of research all on its own. Although the context is ultimately intractable, it is a context automata are well suited for.)
Old-school is beneficial in that it’s nice to have an app product with a decent AI that is under 7mb. (No barrier to download or strong incentive to delete from the device. While the new iPad has up to 128gb, only a small subset of players will be willing to devote significant volume for strong AI, and these players represent a distinct, secondary market segment.) It’s not optimal for an AI take up any more volume than is strictly necessary for a given product.
Fuzzy logic should also be useful for its efficiency in terms of applicability under what would today be considered severe computational restrictions.
[M] games are economic so the model is interesting from a Game Theory standpoint in providing a novel, compact, intrinsic and highly mutable mathematical model based positional valuation in n dimensions in conjunction with stability states in a causal/temporal framework. The combinatorial nature of [M] is ideal for quantitative analysis, and the games involve blocking factors (sudoku) and symmetry breaking (even order gameboards). For players > 2 coalitions also become a factor.
The focus of the procedural research is currently in four main areas and what we’re terming “Adaptive AI” :
Sheer strength is not the goal. We’re working on AI that tailor their strength to their human player’s strength and preferences. For most humans, we don’t want the automata to win more than 2/3 games because always losing is no fun and makes the product less "sticky". Even if the human player desires an automata it cannot beat, the automata should only be sufficiently strong to almost always beat their human. AI strength can be limited by restricting rationality (time and memory), which carries an added benefit of energy conservation (less bits flipped), but the rules-based approach is useful in that rules can be recombined combinatorially to produce automata of different strengths and preferences. Automata play against each other to determine strength hierarchies, and identify poor heuristics to be weeded out of stronger automata.
The automata have to function on an array of related games, where equilibria can be altered in numerous ways without adding mechanics. Additionally, mechanics can be added without altering the nature of the games, such as introducing Graeco-Latin squares. This presents a problem if each configuration has to be learned through intensive self-play because the automata must be able to play at a respectable strengths immediately. Thus the goal is not sheer strength, but consistent strength across the widest array of contexts. (“Respectably weak” and “semi-strong” automata have utility value in that those categorization may be said to describe the majority of human player base.) The idea is an “axiomatic intelligence” that can be extended to include an ever increasing array of contexts.
The automata should not be prone to repetitive play. Initially we’re using limited monte-carlo for positional selection tie-breaking, and the scope can be extend to larger arrays of positions with varying degrees of perceived optimality, up to rational but “counter-intuitive” decisions, which can be subsequently evaluated. This may be useful in adapting to new, dominant strategies that emerge in allowing the automata to experiment with less obvious choices. In situations where the automata is consistently winning, there is incentive to experiment, "investing in loss" in the sense that mistakes are useful from an experience/learning standpoint.
The eventual goal is to implement some form of local reinforcement where the automata learn through play against their human, and self play in restricted contexts, such as between turns when playing against their human. With networking enabled, the automata can play against such automata, with the idea of producing strong automata with human play characteristics. (It will be fun when we eventually put these automata up against pure deep learning algorithms in a wide array of [M] contexts with distinct mathematical properties. My money would be on the ML and NN algorithms in sequential games, but in asynchronous games where there is no turn order, it will be interesting to see if the "axiomatic systems" can produce desirable outcomes by making sound decisions faster than smarter, more complex automata;)