Many learning tasks in adversarial domains tend to be highly dependent on the opponent. Predefined strategies optimized for play against a specific opponent are not likely to succeed when employed against another opponent. Learning a strategy for each new opponent from scratch, though, is inefficient as one is likely to encounter the same or similar opponents again. We call this particular vari...

Form has a strong influence on color perception. We investigated the neural basis of the form-color link in macaque primary visual cortex (V1) by studying orientation selectivity of single V1 cells for pure color patterns. Neurons that responded to color were classified, based on cone inputs and spatial selectivity, into chromatically single-opponent and double-opponent groups. Single-opponent ...

A critical element to successful command and control (C 2) is developing and updating an accurate and lucid model of the interdependencies between functional units, e.g., multiple platoons of artillery and tanks. Two of the challenges to this understanding are (1) the adoption of a detailed description of interdependency and the associated understanding of interdependent functions (Brehmer, 200...

This paper presents a general approach to designing a computer opponent for nondeterministic adversarial games which is able to learn from interaction with a human expert. This approach is based on an integration of planning, knowledge acquisition and learning. We illustrate this approach with WARGLES (WARGame LEarning System), which plays strategic level wargames. WARGLES consists of a game pl...

In Probabilistic Opponent-Model search(PrOM search) the opponent is modelled by a mixed strategy of N opponent types ω0 . . . ωN−1. The opponent is assumed to adopt at every move one of the opponent types ωi according to the probability Pr(ωi). We hypothesize that PrOM search is a better search mechanism than Opponent-Model search (OM search) and Minimax search. In this paper we investigate two...

We introduce a representation for color texture using unichrome and opponent features computed from Gabor filter outputs. The unichrome features are computed from the spectral bands independently while the opponent features combine information across different spectral bands at different scales. Opponent features are motivated by color opponent mechanisms in human vision. We present a method fo...

The agent's capability to acquire, infer, and store the knowledge of other agents is known as agent modeling. Agent modeling addresses problem reasoning about an opponent, which a critical task in competitive situations, or partner, important situations cooperation, communication, enhance social connections. information useful reason intentions, understand its current behavior, predict future b...

Model-based learning of interaction strategies in repeated-games has received a lot of attention in the game-theory literature. Gilboa & Samet [10] deal with bounded regular players. They describe a model-based learning strategy for repeated games that learns the best response against any regular strategy. Their procedure enumerates the set of all automata and chooses the current opponent model...