Agent-based　computing　is　an　important　research　area.　Agent　and　multi-agent　system　can　be　used　to　model　real　systems　in　complex　and　dynamic　environments.　However,　most　of　them　assume　that　there　is　no　uncertain　and　fuzzy　information　in　an　agent＇s　mental　state　and　environment.　In　this　paper,　we　remove　this　unrealistic　assumption　and　propose　a　new　agent　programming　language　which　allows　agent　programs　to　effectively　perform　with　fuzzy　knowledge　under　uncertain　environment,　and　to　dynamically　adapt　with　changes　of　the　environment.　This　language　presents　a　new　and　practical　approach　to　solve　fuzzy　constraints　in　uncertain　environment　which　consists　of　three　components　for　programming　agent:　uncertain　belief　updating,　goal　selection　and　revision　and　uncertain　practical　reasoning.　©　Springer-Verlag　Berlin　Heidelberg　2006.